Tootfinder

ReCatcher: Towards LLMs Regression Testing for Code Generation
Altaf Allah Abbassi, Leuson Da Silva, Amin Nikanjam, Foutse Khomh
https://arxiv.org/abs/2507.19390 https://…

ReCatcher: Towards LLMs Regression Testing for Code Generation
Large Language Models (LLMs) for code generation evolve rapidly through fine-tuning, merging, or new model releases. However, such updates can introduce regressions, not only in correctness but also in code quality and performance. To address this, we present ReCatcher, a regression testing framework for Python code generation. ReCatcher systematically compares two LLMs, typically a current model and a candidate update, across three dimensions: logical correctness, static code quality, and exec…

Orthogonality conditions for convex regression
Sheng Dai, Timo Kuosmanen, Xun Zhou
https://arxiv.org/abs/2506.21110 https://arxiv.org…

Orthogonality conditions for convex regression
Econometric identification generally relies on orthogonality conditions, which usually state that the random error term is uncorrelated with the explanatory variables. In convex regression, the orthogonality conditions for identification are unknown. Applying Lagrangian duality theory, we establish the sample orthogonality conditions for convex regression, including additive and multiplicative formulations of the regression model, with and without monotonicity and homogeneity constraints. We th…

Use multi level models with {parsnip}: http://multilevelmod.tidymodels.org/ #rstats #ML

A Regression-Based Share Market Prediction Model for Bangladesh
Syeda Tasnim Fabiha, Rubaiyat Jahan Mumu, Farzana Aktar, B M Mainul Hossain
https://arxiv.org/abs/2507.18643 http…

A Regression-Based Share Market Prediction Model for Bangladesh
Share market is one of the most important sectors of economic development of a country. Everyday almost all companies issue their shares and investors buy and sell shares of these companies. Generally investors want to buy shares of the companies whose market liquidity is comparatively greater. Market liquidity depends on the average price of a share. In this paper, a thorough linear regression analysis has been performed on the stock market data of Dhaka Stock Exchange. Later, the linear model…

Probabilistic Pretraining for Neural Regression
Boris N. Oreshkin, Shiv Tavker, Dmitry Efimov
https://arxiv.org/abs/2508.16355 https://arxiv.org/pdf/2508.1…

Probabilistic Pretraining for Neural Regression
Transfer learning for probabilistic regression remains underexplored. This work closes this gap by introducing NIAQUE, Neural Interpretable Any-Quantile Estimation, a new model designed for transfer learning in probabilistic regression through permutation invariance. We demonstrate that pre-training NIAQUE directly on diverse downstream regression datasets and fine-tuning it on a specific target dataset enhances performance on individual regression tasks, showcasing the positive impact of proba…

Sparse Polynomial Regression under Anomalous Data
Roozbeh Abolpour, Mohammad Reza Hesamzadeh, Maryam Dehghani
https://arxiv.org/abs/2508.18199 https://arxi…

Sparse Polynomial Regression under Anomalous Data
This paper starts with the general form of the polynomial regression model. We reformulate the Sparse Polynomial Regression Model (SPRM) with anomalous data filtering as Mixed-Integer Linear Program (MILP). This MILP is then converted to a non-convex Quadratically Constrained Quadratic Program (QCQP). Through a proposed mapping, the derived QCQP is reformulated as a Fractional Program (FP). We theoretically show that the reformulated FP has better computational properties than the original QCQP…

The fundamental limits of minimax risk for high-dimensional speckle noise model
Hao Xing, Soham Jana, Arian Maleki
https://arxiv.org/abs/2508.18503 https://

The fundamental limits of minimax risk for high-dimensional speckle noise model
Unlike conventional imaging modalities, such as magnetic resonance imaging, which are often well described by a linear regression framework, coherent imaging systems follow a significantly more complex model. In these systems, the task is to recover the unknown signal or image $\mathbf{x}_o \in \mathbb{R}^n$ from observations $\mathbf{y}_1, \ldots, \mathbf{y}_L \in \mathbb{R}^m$ of the form \[ \mathbf{y}_l = A_l X_o \mathbf{w}_l + \mathbf{z}_l, \quad l = 1, \ldots, L, \] where $X_o = \mathrm{di…

This https://arxiv.org/abs/2502.14479 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Modelling the term-structure of default risk under IFRS 9 within a multistate regression framework
The lifetime behaviour of loans is notoriously difficult to model, which can compromise a bank's financial reserves against future losses, if modelled poorly. Therefore, we present a data-driven comparative study amongst three techniques in modelling a series of default risk estimates over the lifetime of each loan, i.e., its term-structure. The behaviour of loans can be described using a nonstationary and time-dependent semi-Markov model, though we model its elements using a multistate regress…

Quantum Communication Complexity of L2-Regularized Linear Regression Protocols
Sayaki Matsushita
https://arxiv.org/abs/2508.16141 https://arxiv.org/pdf/250…

Quantum Communication Complexity of L2-Regularized Linear Regression Protocols
Linear regression is fundamental to statistical analysis and machine learning, but its application to large-scale datasets necessitates distributed computing. The problem also arises in quantum computing, where handling extensive data requires distributed approaches. This paper investigates distributed linear regression in the quantum coordinator model. Building upon the distributed quantum least squares protocol developed by Montanaro and Shao, we propose improved and extended quantum protocol…

ASP-Assisted Symbolic Regression: Uncovering Hidden Physics in Fluid Mechanics
Theofanis Aravanis, Grigorios Chrimatopoulos, Mohammad Ferdows, Michalis Xenos, Efstratios Em Tzirtzilakis
https://arxiv.org/abs/2507.17777

ASP-Assisted Symbolic Regression: Uncovering Hidden Physics in Fluid Mechanics
Unlike conventional Machine-Learning (ML) approaches, often criticized as "black boxes", Symbolic Regression (SR) stands out as a powerful tool for revealing interpretable mathematical relationships in complex physical systems, requiring no a priori assumptions about models' structures. Motivated by the recognition that, in fluid mechanics, an understanding of the underlying flow physics is as crucial as accurate prediction, this study applies SR to model a fundamental three-dimensional (3D) in…

Integrating Pharmacokinetics and Pharmacodynamics Modeling with Quantum Regression for Predicting Herbal Compound Toxicity
Don Roosan, Saif Nirzhor, Rubayat Khan
https://arxiv.org/abs/2506.20157

Integrating Pharmacokinetics and Pharmacodynamics Modeling with Quantum Regression for Predicting Herbal Compound Toxicity
Herbal compounds present complex toxicity profiles that are often influenced by both intrinsic chemical properties and pharmacokinetics (PK) governing absorption and clearance. In this study, we develop a quantum regression model to predict acute toxicity severity for herbal-derived compounds by integrating toxicity data from NICEATM with pharmacological features from TCMSP.

Unit-Modified Weibull Distribution and Quantile Regression Model
Jo\~ao In\'acio Scrimini, Cleber Bisognin, Renata Rojas Guerra, F\'abio M. Bayer
https://arxiv.org/abs/2508.17359

Unit-Modified Weibull Distribution and Quantile Regression Model
The Sustainable Development Goals (SDGs) of the United Nations consist of 17 general objectives, subdivided into 169 targets to be achieved by 2030. Several SDG indices and indicators require continuous analysis and evaluation, and most of these indices are supported in the unit interval (0,1). To incorporate the flexibility of the modified Weibull (MW) distribution in doubly constrained datasets, the first objective of this work is to propose a new unit probability distribution based on the MW…

Post Hoc Regression Refinement via Pairwise Rankings
Kevin Tirta Wijaya, Michael Sun, Minghao Guo, Hans-Peter Seidel, Wojciech Matusik, Vahid Babaei
https://arxiv.org/abs/2508.16495

Post Hoc Regression Refinement via Pairwise Rankings
Accurate prediction of continuous properties is essential to many scientific and engineering tasks. Although deep-learning regressors excel with abundant labels, their accuracy deteriorates in data-scarce regimes. We introduce RankRefine, a model-agnostic, plug-and-play post hoc method that refines regression with expert knowledge coming from pairwise rankings. Given a query item and a small reference set with known properties, RankRefine combines the base regressor's output with a rank-based e…

FundaQ-8: A Clinically-Inspired Scoring Framework for Automated Fundus Image Quality Assessment
Lee Qi Zun, Oscar Wong Jin Hao, Nor Anita Binti Che Omar, Zalifa Zakiah Binti Asnir, Mohamad Sabri bin Sinal Zainal, Goh Man Fye
https://arxiv.org/abs/2506.20303

FundaQ-8: A Clinically-Inspired Scoring Framework for Automated Fundus Image Quality Assessment
Automated fundus image quality assessment (FIQA) remains a challenge due to variations in image acquisition and subjective expert evaluations. We introduce FundaQ-8, a novel expert-validated framework for systematically assessing fundus image quality using eight critical parameters, including field coverage, anatomical visibility, illumination, and image artifacts. Using FundaQ-8 as a structured scoring reference, we develop a ResNet18-based regression model to predict continuous quality scores…

CPC-CMS: Cognitive Pairwise Comparison Classification Model Selection Framework for Document-level Sentiment Analysis
Jianfei Li, Kevin Kam Fung Yuen
https://arxiv.org/abs/2507.14022

CPC-CMS: Cognitive Pairwise Comparison Classification Model Selection Framework for Document-level Sentiment Analysis
This study proposes the Cognitive Pairwise Comparison Classification Model Selection (CPC-CMS) framework for document-level sentiment analysis. The CPC, based on expert knowledge judgment, is used to calculate the weights of evaluation criteria, including accuracy, precision, recall, F1-score, specificity, Matthews Correlation Coefficient (MCC), Cohen's Kappa (Kappa), and efficiency. Naive Bayes, Linear Support Vector Classification (LSVC), Random Forest, Logistic Regression, Extreme Gradient B…

Binary Response Forecasting under a Factor-Augmented Framework
Tingting Cheng, Jiachen Cong, Fei Liu, Xuanbin Yang
https://arxiv.org/abs/2507.16462 https:/…

Binary Response Forecasting under a Factor-Augmented Framework
In this paper, we propose a novel factor-augmented forecasting regression model with a binary response variable. We develop a maximum likelihood estimation method for the regression parameters and establish the asymptotic properties of the resulting estimators. Monte Carlo simulation results show that the proposed estimation method performs very well in finite samples. Finally, we demonstrate the usefulness of the proposed model through an application to U.S. recession forecasting. The proposed…

(Exhaustive) Symbolic Regression and model selection by minimum description length
Harry Desmond
https://arxiv.org/abs/2507.13033 https://

(Exhaustive) Symbolic Regression and model selection by minimum description length
Symbolic regression is the machine learning method for learning functions from data. After a brief overview of the symbolic regression landscape, I will describe the two main challenges that traditional algorithms face: they have an unknown (and likely significant) probability of failing to find any given good function, and they suffer from ambiguity and poorly-justified assumptions in their function-selection procedure. To address these I propose an exhaustive search and model selection by the…

Zobrist Hash-based Duplicate Detection in Symbolic Regression
Bogdan Burlacu
https://arxiv.org/abs/2508.13859 https://arxiv.org/pdf/2508.13859

Zobrist Hash-based Duplicate Detection in Symbolic Regression
Symbolic regression encompasses a family of search algorithms that aim to discover the best fitting function for a set of data without requiring an a priori specification of the model structure. The most successful and commonly used technique for symbolic regression is Genetic Programming (GP), an evolutionary search method that evolves a population of mathematical expressions through the mechanism of natural selection. In this work we analyze the efficiency of the evolutionary search in GP and…

Fully Few-shot Class-incremental Audio Classification Using Multi-level Embedding Extractor and Ridge Regression Classifier
Yongjie Si, Yanxiong Li, Jiaxin Tan, Qianhua He, Il-Youp Kwak
https://arxiv.org/abs/2506.18406

Fully Few-shot Class-incremental Audio Classification Using Multi-level Embedding Extractor and Ridge Regression Classifier
In the task of Few-shot Class-incremental Audio Classification (FCAC), training samples of each base class are required to be abundant to train model. However, it is not easy to collect abundant training samples for many base classes due to data scarcity and high collection cost. We discuss a more realistic issue, Fully FCAC (FFCAC), in which training samples of both base and incremental classes are only a few. Furthermore, we propose a FFCAC method using a model which is decoupled into a multi…

Bayesian Variable Selection in Multivariate Regression Under Collinearity in the Design Matrix
Joyee Ghosh, Xun Li
https://arxiv.org/abs/2507.17975 https://

Bayesian Variable Selection in Multivariate Regression Under Collinearity in the Design Matrix
We consider the problem of variable selection in Bayesian multivariate linear regression models, involving multiple response and predictor variables, under multivariate normal errors. In the absence of a known covariance structure, specifying a model with a non-diagonal covariance matrix is appealing. Modeling dependency in the random errors through a non-diagonal covariance matrix is generally expected to lead to improved estimation of the regression coefficients. In this article, we highlight…

Refining Tc Prediction in Hydrides via Symbolic-Regression-Enhanced Electron-Localization-Function-Based Descriptors
Francesco Belli, Sean Torres, Julia Contreras-Garc\`ia, Eva Zurek
https://arxiv.org/abs/2506.17456

Refining Tc Prediction in Hydrides via Symbolic-Regression-Enhanced Electron-Localization-Function-Based Descriptors
Hydrogen-based materials are able to possess extremely high superconducting critical temperatures, \tc s, due to hydrogen's low atomic mass and strong electron-phonon interaction. Recently, a descriptor based on the Electron Localization Function (ELF) has enabled the rapid estimation of the \tc\ of hydrogen-containing compounds from electronic networking properties, but its applicability has been limited by the small size and homogeneity of the training dataset used. Herein, the model is re-ex…

Fast and Efficient Implementation of the Maximum Likelihood Estimation for the Linear Regression with Gaussian Model Uncertainty
Ruohai Guo, Jiang Zhu, Xing Jiang, Fengzhong Qu
https://arxiv.org/abs/2507.11249

Fast and Efficient Implementation of the Maximum Likelihood Estimation for the Linear Regression with Gaussian Model Uncertainty
The linear regression model with a random variable (RV) measurement matrix, where the mean of the random measurement matrix has full column rank, has been extensively studied. In particular, the quasiconvexity of the maximum likelihood estimation (MLE) problem was established, and the corresponding Cramer-Rao bound (CRB) was derived, leading to the development of an efficient bisection-based algorithm known as RV-ML. In contrast, this work extends the analysis to both overdetermined and underde…

Jet Reconstruction with Mamba Networks in Collider Events
Jinmian Li, Peng Li, Bingwei Long, Rao Zhang
https://arxiv.org/abs/2506.18336 https://

Jet Reconstruction with Mamba Networks in Collider Events
We introduce a novel end-to-end framework for jet reconstruction in high-energy collider events, leveraging the efficiency and long-range modeling capabilities of the Mamba architecture. Our model unifies instance segmentation, classification, and kinematic regression into a single multi-task learning system, enabling a sophisticated multi-level reconstruction that simultaneously identifies primary heavy jets ($t$, $H$, $W/Z$) and their constituent sub-jets. To facilitate supervised learning fo…

Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces
Marcos Matabuena, Rahul Ghosal, Pavlo Mozharovskyi, Oscar Hernan Madrid Padilla, Jukka-Pekka Onnela
https://arxiv.org/abs/2506.08325

Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces
Depth measures are powerful tools for defining level sets in emerging, non--standard, and complex random objects such as high-dimensional multivariate data, functional data, and random graphs. Despite their favorable theoretical properties, the integration of depth measures into regression modeling to provide prediction regions remains a largely underexplored area of research. To address this gap, we propose a novel, model-free uncertainty quantification algorithm based on conditional depth mea…

Cosmic Distance Duality Relation with DESI DR2 and Transparency
Xuwei Zhang, Xiaofeng Yang, Yunliang Ren, Shuangnan Chen, Yangjun Shi, Cheng Cheng, Xiaolong He
https://arxiv.org/abs/2506.17926

Cosmic Distance Duality Relation with DESI DR2 and Transparency
The Cosmic Distance Duality Relation (CDDR), a fundamental assumption in modern cosmology, posits a direct link between angular diameter distance and luminosity distance. This study presents a comprehensive, model-independent, and data-driven test of the CDDR using a combination of cosmological observations, including Supernovae (SN), Baryon Acoustic Oscillations (BAO), and Hubble parameter ($H(z)$) measurements. We employ both Gaussian Process Regression (GPR) and a novel Compressed Point (CPI…

Robust PDE discovery under sparse and highly noisy conditions via attention neural networks
Shilin Zhang, Yunqing Huang, Nianyu Yi, shihan Zhang
https://arxiv.org/abs/2506.17908

Robust PDE discovery under sparse and highly noisy conditions via attention neural networks
The discovery of partial differential equations (PDEs) from experimental data holds great promise for uncovering predictive models of complex physical systems. In this study, we introduce an efficient automatic model discovery framework, ANN-PYSR, which integrates attention neural networks with the state-of-the-art PySR symbolic regression library. Our approach successfully identifies the governing PDE in six benchmark examples. Compared to the DLGA framework, numerical experiments demonstrate …

Predicting Stock Market Crash with Bayesian Generalised Pareto Regression
Sourish Das
https://arxiv.org/abs/2506.17549 https://arxiv.…

Predicting Stock Market Crash with Bayesian Generalised Pareto Regression
This paper develops a Bayesian Generalised Pareto Regression (GPR) model to forecast extreme losses in Indian equity markets, with a focus on the Nifty 50 index. Extreme negative returns, though rare, can cause significant financial disruption, and accurate modelling of such events is essential for effective risk management. Traditional Generalised Pareto Distribution (GPD) models often ignore market conditions; in contrast, our framework links the scale parameter to covariates using a log-line…

Symbolic Regression-Enhanced Dynamic Wake Meandering: Fast and Physically Consistent Wind-Turbine Wake Modeling
Ding Wang, Dachuan Feng, Kangcheng Zhou, Yuntian Chen, Shijun Liao, Shiyi Chen
https://arxiv.org/abs/2506.14403

Symbolic Regression-Enhanced Dynamic Wake Meandering: Fast and Physically Consistent Wind-Turbine Wake Modeling
Accurately modeling wind turbine wakes is essential for optimizing wind farm performance but remains a persistent challenge. While the dynamic wake meandering (DWM) model captures unsteady wake behavior, it suffers from near-wake inaccuracies due to empirical closures. We propose a Symbolic Regression-enhanced DWM (SRDWM) framework that achieves equation-level closure by embedding symbolic expressions for volumetric forcing and boundary terms explicitly into governing equations. These physicall…

Analysis of Photonic Circuit Losses with Machine Learning Techniques
Adrian Nugraha Utama, Simon Chun Kiat Goh, Li Hongyu, Wang Xiangyu, Zhou Yanyan, Victor Leong, Manas Mukherjee
https://arxiv.org/abs/2506.17999

Analysis of Photonic Circuit Losses with Machine Learning Techniques
Low-loss waveguides enable efficient light delivery in photonic circuits, which are essential for high-speed optical communications and scalable implementations of photonic quantum technologies. We study the effects of several fabrication and experimental parameters on the waveguide losses of a silicon nitride integrated photonics platform using various machine learning techniques. Compared to more complex machine learning algorithms, our results show that a simple linear regression model with …

Empirical Models of the Time Evolution of SPX Option Prices
Alessio Brini, David A. Hsieh, Patrick Kuiper, Sean Moushegian, David Ye
https://arxiv.org/abs/2506.17511

Empirical Models of the Time Evolution of SPX Option Prices
The key objective of this paper is to develop an empirical model for pricing SPX options that can be simulated over future paths of the SPX. To accomplish this, we formulate and rigorously evaluate several statistical models, including neural network, random forest, and linear regression. These models use the observed characteristics of the options as inputs -- their price, moneyness and time-to-maturity, as well as a small set of external inputs, such as the SPX and its past history, dividend …

Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression
Samuel Stocksieker, Denys pommeret, Arthur Charpentier
https://arxiv.org/abs/2508.13829 https://

Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression
Imbalanced distribution learning is a common and significant challenge in predictive modeling, often reducing the performance of standard algorithms. Although various approaches address this issue, most are tailored to classification problems, with a limited focus on regression. This paper introduces a novel method to improve learning on tabular data within the Imbalanced Regression (IR) framework, which is a critical problem. We propose using Variational Autoencoders (VAEs) to model and define…

A Robust Controller based on Gaussian Processes for Robotic Manipulators with Unknown Uncertainty
Giulio Giacomuzzo, Mohamed Abdelwahab, Marco Cal\`i, Alberto Dalla Libera, Ruggero Carli
https://arxiv.org/abs/2507.11170

A Robust Controller based on Gaussian Processes for Robotic Manipulators with Unknown Uncertainty
In this paper, we propose a novel learning-based robust feedback linearization strategy to ensure precise trajectory tracking for an important family of Lagrangian systems. We assume a nominal knowledge of the dynamics is given but no a-priori bounds on the model mismatch are available. In our approach, the key ingredient is the adoption of a regression framework based on Gaussian Processes (GPR) to estimate the model mismatch. This estimate is added to the outer loop of a classical feedback li…

Multinomial probit model based on joint quantile regression
Masaaki Okabe, Koki Matsuoka, Jun Tsuchida, Hiroshi Yadohisa
https://arxiv.org/abs/2508.13556 https://

Multinomial probit model based on joint quantile regression
The multinomial probit model is a typical statistical model for multiple-choice data applied in many research areas. When we are interested in some quantiles of relative utilities for understanding the distribution of these utilities, the multinomial probit model is unsuitable because we only interpret the expectation of relative utilities based on it. We thus propose quantile regression analysis methods for multinomial choice data based on joint quantile regression and multinomial probit model…

Finetuning Large Language Model as an Effective Symbolic Regressor
Yingfan Hua, Ruikun Li, Jun Yao, Guohang Zhuang, Shixiang Tang, Bin Liu, Wanli Ouyang, Yan Lu
https://arxiv.org/abs/2508.09897

Finetuning Large Language Model as an Effective Symbolic Regressor
Deriving governing equations from observational data, known as Symbolic Regression (SR), is a cornerstone of scientific discovery. Large Language Models (LLMs) have shown promise in this task by leveraging their vast cross-disciplinary scientific knowledge. However, existing LLM-based methods primarily rely on direct inference or prompt engineering, often requiring excessive inference iterations to converge on correct formulas or failing to treating complex equation targets. These limitations i…

Asymptotic confidence bands for the histogram regression estimator
Natalie Neumeyer, Jan Rabe, Mathias Trabs
https://arxiv.org/abs/2508.12391 https://arxiv…

Asymptotic confidence bands for the histogram regression estimator
In a multivariate nonparametric regression model with a Hölder continuous regression function and heteroscedastic noise asymptotic uniform confidence bands are constructed based on the histogram estimator. The radius of the confidence bands does not depend on an extreme value distribution, but instead can be numerically calculated for the chosen binning.

Revisiting Randomization in Greedy Model Search
Xin Chen, Jason M. Klusowski, Yan Shuo Tan, Chang Yu
https://arxiv.org/abs/2506.15643 https://

Revisiting Randomization in Greedy Model Search
Combining randomized estimators in an ensemble, such as via random forests, has become a fundamental technique in modern data science, but can be computationally expensive. Furthermore, the mechanism by which this improves predictive performance is poorly understood. We address these issues in the context of sparse linear regression by proposing and analyzing an ensemble of greedy forward selection estimators that are randomized by feature subsampling -- at each iteration, the best feature is s…

Swap Regression Methodology for Predicting Relationship with Historical Bivariate Data
Viral Chitlangia, Mosuk Chow, Sharmishtha Mitra
https://arxiv.org/abs/2508.15479 https://

Swap Regression Methodology for Predicting Relationship with Historical Bivariate Data
This study revisits regression for samples with alternating predictors (SWAP) proposed in Chow et al.[2015] with the purpose of finding the best fit model when the role of the response and the explanatory variables was established. In the current work, we explore the directional relationship between the two variables at a given point of time, by a novel approach which draws direct inspiration from the concept of SWAP regression. Our method, based on the Gaussian Mixture Model (GMM) and the beta…

Feature-free regression kriging
Peng Luo, Yilong Wu, Yongze Song
https://arxiv.org/abs/2507.07382 https://arxiv.org/pdf/2507.07382

Feature-free regression kriging
Spatial interpolation is a crucial task in geography. As perhaps the most widely used interpolation methods, geostatistical models -- such as Ordinary Kriging (OK) -- assume spatial stationarity, which makes it difficult to capture the nonstationary characteristics of geographic variables. A common solution is trend surface modeling (e.g., Regression Kriging, RK), which relies on external explanatory variables to model the trend and then applies geostatistical interpolation to the residuals. Ho…

UPLME: Uncertainty-Aware Probabilistic Language Modelling for Robust Empathy Regression
Md Rakibul Hasan, Md Zakir Hossain, Aneesh Krishna, Shafin Rahman, Tom Gedeon
https://arxiv.org/abs/2508.03520

UPLME: Uncertainty-Aware Probabilistic Language Modelling for Robust Empathy Regression
Supervised learning for empathy regression is challenged by noisy self-reported empathy scores. While many algorithms have been proposed for learning with noisy labels in textual classification problems, the regression counterpart is relatively under-explored. We propose UPLME, an uncertainty-aware probabilistic language modelling framework to capture label noise in the regression setting of empathy detection. UPLME includes a probabilistic language model that predicts both empathy score and he…

Calibration offset estimation in mobile hearing tests via categorical loudness scaling
Chen Xu, Birger Kollmeier
https://arxiv.org/abs/2508.14824 https://a…

Calibration offset estimation in mobile hearing tests via categorical loudness scaling
Objective: To enable reliable smartphone-based hearing assessments by developing methods to estimate device calibration offsets using categorical loudness scaling (CLS). Design: Calibration offsets were simulated from a Gaussian distribution. Two prediction models - a Bayesian regression model and a nearest neighbor model - were trained on CLS-derived parameters and data from the Oldenburg Hearing Health Repository (OHHR). CLS was chosen because it provides level-independent measures (e.g., dyn…

Gradient Boosting for Spatial Regression Models with Autoregressive Disturbances
Michael Balzer
https://arxiv.org/abs/2506.13682 https://

Gradient Boosting for Spatial Regression Models with Autoregressive Disturbances
Researchers in urban and regional studies increasingly deal with spatial data that reflects geographic location and spatial relationships. As a framework for dealing with the unique nature of spatial data, various spatial regression models have been introduced. In this article, a novel model-based gradient boosting algorithm for spatial regression models with autoregressive disturbances is proposed. Due to the modular nature, the approach provides an alternative estimation procedure which is fe…

Efficient data-driven regression for reduced-order modeling of spatial pattern formation
Alessandro Alla, Rudy Geelen, Hannah Lu
https://arxiv.org/abs/2508.06833 https://…

Efficient data-driven regression for reduced-order modeling of spatial pattern formation
We present an efficient data-driven regression approach for constructing reduced-order models (ROMs) of reaction-diffusion systems exhibiting pattern formation. The ROMs are learned non-intrusively from available training data of physically accurate numerical simulations. The method can be applied to general nonlinear systems through the use of polynomial model form, while not requiring knowledge of the underlying physical model, governing equations, or numerical solvers. The process of learnin…

Data-Driven Nonlinear Regulation: Gaussian Process Learning
Telema Harry, Martin Guay, Shimin Wang, Richard D. Braatz
https://arxiv.org/abs/2506.09273 http…

Data-Driven Nonlinear Regulation: Gaussian Process Learning
This article addresses the output regulation problem for a class of nonlinear systems using a data-driven approach. An output feedback controller is proposed that integrates a traditional control component with a data-driven learning algorithm based on Gaussian Process (GP) regression to learn the nonlinear internal model. Specifically, a data-driven technique is employed to directly approximate the unknown internal model steady-state map from observed input-output data online. Our method does …

An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data: A Case Study of Indonesia
Wishnu Badrawani
https://arxiv.org/abs/2506.10369

An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data: A Case Study of Indonesia
This paper evaluates the performance of prominent machine learning (ML) algorithms in predicting Indonesia's inflation using the payment system, capital market, and macroeconomic data. We compare the forecasting performance of each ML model, namely shrinkage regression, ensemble learning, and super vector regression, to that of the univariate time series ARIMA and SARIMA models. We examine various out-of-bag sample periods in each ML model to determine the appropriate data-splitting ratios for …

ICAS: Detecting Training Data from Autoregressive Image Generative Models
Hongyao Yu, Yixiang Qiu, Yiheng Yang, Hao Fang, Tianqu Zhuang, Jiaxin Hong, Bin Chen, Hao Wu, Shu-Tao Xia
https://arxiv.org/abs/2507.05068

ICAS: Detecting Training Data from Autoregressive Image Generative Models
Autoregressive image generation has witnessed rapid advancements, with prominent models such as scale-wise visual auto-regression pushing the boundaries of visual synthesis. However, these developments also raise significant concerns regarding data privacy and copyright. In response, training data detection has emerged as a critical task for identifying unauthorized data usage in model training. To better understand the vulnerability of autoregressive image generative models to such detection, …

A symbolic regression-based implicit algebraic stress turbulence model: incorporating the production of non-dimensional Reynolds stress deviatoric tensor
Ziqi Ji, Penghao Duan, Gang Du
https://arxiv.org/abs/2507.04679

A symbolic regression-based implicit algebraic stress turbulence model: incorporating the production of non-dimensional Reynolds stress deviatoric tensor
Turbulence constitutes an exceptionally complex and irregular flow phenomenon that manifests in liquids, gases, and plasma, making it ubiquitous in both natural processes and engineering applications. Given the relatively modest advancements in classical turbulence models over the past half-century, data-driven approaches, such as machine learning, have recently gained considerable traction in turbulence model research. In this study, we introduce a symbolic regression-based implicit algebraic …

Let the Tree Decide: FABART A Non-Parametric Factor Model
Sofia Velasco
https://arxiv.org/abs/2506.11551 https://arxiv.org/pdf/2506.1…

Let the Tree Decide: FABART A Non-Parametric Factor Model
This article proposes a novel framework that integrates Bayesian Additive Regression Trees (BART) into a Factor-Augmented Vector Autoregressive (FAVAR) model to forecast macro-financial variables and examine asymmetries in the transmission of oil price shocks. By employing nonparametric techniques for dimension reduction, the model captures complex, nonlinear relationships between observables and latent factors that are often missed by linear approaches. A simulation experiment comparing FABART…

Projection-based multifidelity linear regression for data-scarce applications
Vignesh Sella, Julie Pham, Karen Willcox, Anirban Chaudhuri
https://arxiv.org/abs/2508.08517 https:…

Projection-based multifidelity linear regression for data-scarce applications
Surrogate modeling for systems with high-dimensional quantities of interest remains challenging, particularly when training data are costly to acquire. This work develops multifidelity methods for multiple-input multiple-output linear regression targeting data-limited applications with high-dimensional outputs. Multifidelity methods integrate many inexpensive low-fidelity model evaluations with limited, costly high-fidelity evaluations. We introduce two projection-based multifidelity linear reg…

Constraint-Guided Symbolic Regression for Data-Efficient Kinetic Model Discovery
Miguel \'Angel de Carvalho Servia (Mimi), Ilya Orson Sandoval (Mimi), King Kuok (Mimi), Hii, Klaus Hellgardt, Dongda Zhang, Ehecatl Antonio del Rio Chanona
https://arxiv.org/abs/2507.02730

Constraint-Guided Symbolic Regression for Data-Efficient Kinetic Model Discovery
The industrialization of catalytic processes hinges on the availability of reliable kinetic models for design, optimization, and control. Traditional mechanistic models demand extensive domain expertise, while many data-driven approaches often lack interpretability and fail to enforce physical consistency. To overcome these limitations, we propose the Physics-Informed Automated Discovery of Kinetics (PI-ADoK) framework. By integrating physical constraints directly into a symbolic regression app…

Average quantile regression: a new non-mean regression model and coherent risk measure
Rong Jiang, M. C. Jones, Keming Yu, Jiangfeng Wang
https://arxiv.org/abs/2506.23059

Average quantile regression: a new non-mean regression model and coherent risk measure
Regression models that go beyond the mean, alongside coherent risk measures, have been important tools in modern data analysis. This paper introduces the innovative concept of Average Quantile Regression (AQR), which is smooth at the quantile-like level, comonotonically additive, and explicitly accounts for the severity of tail losses relative to quantile regression. AQR serves as a versatile regression model capable of describing distributional information across all positions, akin to quantil…

High-dimensional regression with outcomes of mixed-type using the multivariate spike-and-slab LASSO
Soham Ghosh, Sameer K. Deshpande
https://arxiv.org/abs/2506.13007

High-dimensional regression with outcomes of mixed-type using the multivariate spike-and-slab LASSO
We consider a high-dimensional multi-outcome regression in which $q,$ possibly dependent, binary and continuous outcomes are regressed onto $p$ covariates. We model the observed outcome vector as a partially observed latent realization from a multivariate linear regression model. Our goal is to estimate simultaneously a sparse matrix ($B$) of latent regression coefficients (i.e., partial covariate effects) and a sparse latent residual precision matrix ($Ω$), which induces partial correlations …

Leveraging Large Language Models for Predictive Analysis of Human Misery
Bishanka Seal, Rahul Seetharaman, Aman Bansal, Abhilash Nandy
https://arxiv.org/abs/2508.12669 https://

Leveraging Large Language Models for Predictive Analysis of Human Misery
This study investigates the use of Large Language Models (LLMs) for predicting human-perceived misery scores from natural language descriptions of real-world scenarios. The task is framed as a regression problem, where the model assigns a scalar value from 0 to 100 to each input statement. We evaluate multiple prompting strategies, including zero-shot, fixed-context few-shot, and retrieval-based prompting using BERT sentence embeddings. Few-shot approaches consistently outperform zero-shot base…

Discovering the underlying analytic structure within Standard Model constants using artificial intelligence
S. V. Chekanov, H. Kjellerstrand
https://arxiv.org/abs/2507.00225

Discovering the underlying analytic structure within Standard Model constants using artificial intelligence
This paper presents a search for underlying analytic structures among the fundamental parameters of the Standard Model (SM) using symbolic regression and genetic programming. We identify the simplest analytic relationships connecting pairs of these constants and report several notable observations based on about a thousand expressions with relative precision better than 1%. These results may serve as valuable inputs for model builders and artificial intelligence methods aimed at uncovering hidd…

Identifying Optimal Regression Models For DEM Simulation Datasets
B. D. Jenkins, A. L. Nicusan, A. Neveu, G. Lumay, F. Francqui, J. P. K. Seville, C. R. K. Windows-Yule
https://arxiv.org/abs/2508.05308

Identifying Optimal Regression Models For DEM Simulation Datasets
Developing fast regression models (surrogate/metamodels) from DEM data is key for practical industrial application to allow real-time evaluations. However, benchmarking different models is often overlooked in particle technology for regression tasks, as model selection is frequently not the primary research focus. This can lead to the use of suboptimal models, resulting in subpar predictive accuracy, slow evaluations, or poor generalisation, hindering effective real-time decision-making and pro…

TRUST: Transparent, Robust and Ultra-Sparse Trees
Albert Dorador
https://arxiv.org/abs/2506.15791 https://arxiv.org/pdf/2506.15791

TRUST: Transparent, Robust and Ultra-Sparse Trees
Piecewise-constant regression trees remain popular for their interpretability, yet often lag behind black-box models like Random Forest in predictive accuracy. In this work, we introduce TRUST (Transparent, Robust, and Ultra-Sparse Trees), a novel regression tree model that combines the accuracy of Random Forests with the interpretability of shallow decision trees and sparse linear models. TRUST further enhances transparency by leveraging Large Language Models to generate tailored, user-friendl…

A Self-scaled Approximate $\ell_0$ Regularization Robust Model for Outlier Detection
Pengyang Song, Jue Wang
https://arxiv.org/abs/2506.22277 https://

A Self-scaled Approximate $\ell_0$ Regularization Robust Model for Outlier Detection
Robust regression models in the presence of outliers have significant practical relevance in areas such as signal processing, financial econometrics, and energy management. Many existing robust regression methods, either grounded in statistical theory or sparse signal recovery, typically rely on the explicit or implicit assumption of outlier sparsity to filter anomalies and recover the underlying signal or data. However, these methods often suffer from limited robustness or high computational c…

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective
Roger Guimera, Marta Sales-Pardo
https://arxiv.org/abs/2507.19540 https://

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective
Symbolic regression automates the process of learning closed-form mathematical models from data. Standard approaches to symbolic regression, as well as newer deep learning approaches, rely on heuristic model selection criteria, heuristic regularization, and heuristic exploration of model space. Here, we discuss the probabilistic approach to symbolic regression, an alternative to such heuristic approaches with direct connections to information theory and statistical physics. We show how the prob…

A Nonparametric Approach to Augmenting a Bayesian VAR with Nonlinear Factors
Todd Clark, Florian Huber, Gary Koop
https://arxiv.org/abs/2508.13972 https://…

A Nonparametric Approach to Augmenting a Bayesian VAR with Nonlinear Factors
This paper proposes a Vector Autoregression augmented with nonlinear factors that are modeled nonparametrically using regression trees. There are four main advantages of our model. First, modeling potential nonlinearities nonparametrically lessens the risk of mis-specification. Second, the use of factor methods ensures that departures from linearity are modeled parsimoniously. In particular, they exhibit functional pooling where a small number of nonlinear factors are used to model common nonli…

A critical assessment of machine learning in fluid dynamics
Kunihiko Taira, Georgios Rigas, Kai Fukami
https://arxiv.org/abs/2508.13430 https://arxiv.org/p…

A critical assessment of machine learning in fluid dynamics
The fluid dynamics community has increasingly adopted machine learning to analyze, model, predict, and control a wide range of flows. These methods offer powerful computational capabilities for regression, compression, and optimization. In some cases, machine learning has even outperformed traditional approaches. However, many fluid mechanics problems remain beyond the reach of current machine learning techniques. As the field moves from its current state toward a more mature paradigm, this art…

Oldies but Goldies: The Potential of Character N-grams for Romanian Texts
Dana Lupsa, Sanda-Maria Avram
https://arxiv.org/abs/2506.15650 https://

Oldies but Goldies: The Potential of Character N-grams for Romanian Texts
This study addresses the problem of authorship attribution for Romanian texts using the ROST corpus, a standard benchmark in the field. We systematically evaluate six machine learning techniques: Support Vector Machine (SVM), Logistic Regression (LR), k-Nearest Neighbors (k-NN), Decision Trees (DT), Random Forests (RF), and Artificial Neural Networks (ANN), employing character n-gram features for classification. Among these, the ANN model achieved the highest performance, including perfect clas…

This https://arxiv.org/abs/2502.05210 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Regression and Forecasting of U.S. Stock Returns Based on LSTM
This paper analyses the investment returns of three stock sectors, Manuf, Hitec, and Other, in the U.S. stock market, based on the Fama-French three-factor model, the Carhart four-factor model, and the Fama-French five-factor model, in order to test the validity of the Fama-French three-factor model, the Carhart four-factor model, and the Fama-French five-factor model for the three sectors of the market. French five-factor model for the three sectors of the market. Also, the LSTM model is used …

This https://arxiv.org/abs/2312.01602 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Quantum Time Series Similarity Measures and Quantum Temporal Kernels
This article presents a quantum computing approach to designing of similarity measures and kernels for classification of stochastic symbolic time series. In the area of machine learning, kernels are important components of various similarity-based classification, clustering, and regression algorithms. An effective strategy for devising problem-specific kernels is leveraging existing generative models of the example space. In this study we assume that a quantum generative model, known as quantum…

Primer to get you started with Optimization and Mathematical Programming in R #rstats

A Framework for Nonstationary Gaussian Processes with Neural Network Parameters
Zachary James, Joseph Guinness
https://arxiv.org/abs/2507.12262 https://

A Framework for Nonstationary Gaussian Processes with Neural Network Parameters
Gaussian processes have become a popular tool for nonparametric regression because of their flexibility and uncertainty quantification. However, they often use stationary kernels, which limit the expressiveness of the model and may be unsuitable for many datasets. We propose a framework that uses nonstationary kernels whose parameters vary across the feature space, modeling these parameters as the output of a neural network that takes the features as input. The neural network and Gaussian proce…

A Framework for Creating Non-Regressive Test Cases via Branch Consistency Analysis Driven by Descriptions
Yuxiang Zhang, Pengyu Xue, Zhen Yang, Xiaoxue Ren, Xiang Li, Linhao Wu, Jiancheng Zhao, Xingda Yu
https://arxiv.org/abs/2506.07486

A Framework for Creating Non-Regressive Test Cases via Branch Consistency Analysis Driven by Descriptions
Automated test-generation research overwhelmingly assumes the correctness of focal methods, yet practitioners routinely face non-regression scenarios where the focal method may be defective. A baseline evaluation of EvoSuite and two leading Large Language Model (LLM)-based generators, namely ChatTester and ChatUniTest, on defective focal methods reveals that despite achieving up to 83% of branch coverage, none of the generated tests expose defects. To resolve this problem, we first construct …

Residual 1D CNN for Low SFR Surface Density Regression: A Design Note
Po-Chieh Yu
#toXiv_bot_toot

Debiased Prediction Inference with Non-sparse Loadings in Misspecified High-dimensional Regression Models
Libin Liang, Zhiqiang Tan
https://arxiv.org/abs/2507.10944

Debiased Prediction Inference with Non-sparse Loadings in Misspecified High-dimensional Regression Models
High-dimensional regression models with regularized sparse estimation are widely applied. For statistical inferences, debiased methods are available about single coefficients or predictions with sparse new covariate vectors (also called loadings), in the presence of possible model misspecification. However, statistical inferences about predictions with non-sparse loadings are studied only under the assumption of correctly specified models. In this work, we develop debiased estimation and associ…

Nonlinear projection-based model order reduction with machine learning regression for closure error modeling in the latent space
S. Ares de Parga, Radek Tezaur, Carlos G. Hern\'andez, Charbel Farhat
https://arxiv.org/abs/2507.00634

Nonlinear projection-based model order reduction with machine learning regression for closure error modeling in the latent space
A significant advancement in nonlinear projection-based model order reduction (PMOR) is presented through a highly effective methodology. This methodology employs Gaussian process regression (GPR) and radial basis function (RBF) interpolation for closure error modeling in the latent space, offering notable gains in efficiency and expanding the scope of PMOR. Moving beyond the limitations of deep artificial neural networks (ANNs), previously used for this task, this approach provides crucial adv…

Akaike information criterion for segmented regression models
Kazuki Nakajima, Yoshiyuki Ninomiya
https://arxiv.org/abs/2506.08760 https://

Akaike information criterion for segmented regression models
In segmented regression, when the regression function is continuous at the change-points that are the boundaries of the segments, it is also called joinpoint regression, and the analysis package developed by \cite{KimFFM00} has become a standard tool for analyzing trends in longitudinal data in the field of epidemiology. In addition, it is sometimes natural to expect the regression function to be discontinuous at the change-points, and in the field of epidemiology, this model is used in \cite{J…

This https://arxiv.org/abs/2506.01348 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Distributionally Robust Learning in Survival Analysis
We introduce an innovative approach that incorporates a Distributionally Robust Learning (DRL) approach into Cox regression to enhance the robustness and accuracy of survival predictions. By formulating a DRL framework with a Wasserstein distance-based ambiguity set, we develop a variant Cox model that is less sensitive to assumptions about the underlying data distribution and more resilient to model misspecification and data perturbations. By leveraging Wasserstein duality, we reformulate the …

Estimating the Number of Components in Panel Data Finite Mixture Regression Models with an Application to Production Function Heterogeneity
Yu Hao, Hiroyuki Kasahara
https://arxiv.org/abs/2506.09666

Estimating the Number of Components in Panel Data Finite Mixture Regression Models with an Application to Production Function Heterogeneity
This paper develops statistical methods for determining the number of components in panel data finite mixture regression models with regression errors independently distributed as normal or more flexible normal mixtures. We analyze the asymptotic properties of the likelihood ratio test (LRT) and information criteria (AIC and BIC) for model selection in both conditionally independent and dynamic panel settings. Unlike cross-sectional normal mixture models, we show that panel data structures elim…

Test of partial effects for Frechet regression on Bures-Wasserstein manifolds
Haoshu Xu, Hongzhe Li
https://arxiv.org/abs/2506.23487 https://

Test of partial effects for Frechet regression on Bures-Wasserstein manifolds
We propose a novel test for assessing partial effects in Frechet regression on Bures Wasserstein manifolds. Our approach employs a sample splitting strategy: the first subsample is used to fit the Frechet regression model, yielding estimates of the covariance matrices and their associated optimal transport maps, while the second subsample is used to construct the test statistic. We prove that this statistic converges in distribution to a weighted mixture of chi squared components, where the wei…

R2 priors for Grouped Variance Decomposition in High-dimensional Regression
Javier Enrique Aguilar, David Kohns, Aki Vehtari, Paul-Christian B\"urkner
https://arxiv.org/abs/2507.11833

R2 priors for Grouped Variance Decomposition in High-dimensional Regression
We introduce the Group-R2 decomposition prior, a hierarchical shrinkage prior that extends R2-based priors to structured regression settings with known groups of predictors. By decomposing the prior distribution of the coefficient of determination R2 in two stages, first across groups, then within groups, the prior enables interpretable control over model complexity and sparsity. We derive theoretical properties of the prior, including marginal distributions of coefficients, tail behavior, and …

Non-asymptotic convergence bound of conditional diffusion models
Mengze Li
https://arxiv.org/abs/2508.10944 https://arxiv.org/pdf/2508.10944

Non-asymptotic convergence bound of conditional diffusion models
Learning and generating various types of data based on conditional diffusion models has been a research hotspot in recent years. Although conditional diffusion models have made considerable progress in improving acceleration algorithms and enhancing generation quality, the lack of non-asymptotic properties has hindered theoretical research. To address this gap, we focus on a conditional diffusion model within the domains of classification and regression (CARD), which aims to learn the original …

Stealing Accuracy: Predicting Day-ahead Electricity Prices with Temporal Hierarchy Forecasting (THieF)
Arkadiusz Lipiecki, Kaja Bilinska, Nicolaos Kourentzes, Rafal Weron
https://arxiv.org/abs/2508.11372

Stealing Accuracy: Predicting Day-ahead Electricity Prices with Temporal Hierarchy Forecasting (THieF)
We introduce the concept of temporal hierarchy forecasting (THieF) in predicting day-ahead electricity prices and show that reconciling forecasts for hourly products, 2- to 12-hour blocks, and baseload contracts significantly (up to 13%) improves accuracy at all levels. These results remain consistent throughout a challenging 4-year test period (2021-2024) in the German power market and across model architectures, including linear regression, a shallow neural network, gradient boosting, and a s…

Diffusion index forecasts under weaker loadings: PCA, ridge regression, and random projections
Tom Boot, Bart Keijsers
https://arxiv.org/abs/2506.09575 htt…

Diffusion index forecasts under weaker loadings: PCA, ridge regression, and random projections
We study the accuracy of forecasts in the diffusion index forecast model with possibly weak loadings. The default option to construct forecasts is to estimate the factors through principal component analysis (PCA) on the available predictor matrix, and use the estimated factors to forecast the outcome variable. Alternatively, we can directly relate the outcome variable to the predictors through either ridge regression or random projections. We establish that forecasts based on PCA, ridge regres…

Generative Flexible Latent Structure Regression (GFLSR) model
Clara Grazian, Qian Jin, Pierre Lafaye De Micheaux
https://arxiv.org/abs/2508.04393 https://a…

Generative Flexible Latent Structure Regression (GFLSR) model
Latent structure methods, specifically linear continuous latent structure methods, are a type of fundamental statistical learning strategy. They are widely used for dimension reduction, regression and prediction, in the fields of chemometrics, economics, social science and etc. However, due to the lack of model inference, generative form, and unidentifiable parameters, most of these methods are always used as an algorithm, instead of a model. This paper proposed a Generative Flexible Latent Str…

Galerkin-ARIMA: A Two-Stage Polynomial Regression Framework for Fast Rolling One-Step-Ahead Forecasting
Haojie Liu, Zihan Lin
https://arxiv.org/abs/2507.07469

Galerkin-ARIMA: A Two-Stage Polynomial Regression Framework for Fast Rolling One-Step-Ahead Forecasting
Time-series models like ARIMA remain widely used for forecasting but limited to linear assumptions and high computational cost in large and complex datasets. We propose Galerkin-ARIMA that generalizes the AR component of ARIMA and replace it with a flexible spline-based function estimated by Galerkin projection. This enables the model to capture nonlinear dependencies in lagged values and retain the MA component and Gaussian noise assumption. We derive a closed-form OLS estimator for the Galerk…

Multi-Task Reward Learning from Human Ratings
Mingkang Wu, Devin White, Evelyn Rose, Vernon Lawhern, Nicholas R Waytowich, Yongcan Cao
https://arxiv.org/abs/2506.09183

Multi-Task Reward Learning from Human Ratings
Reinforcement learning from human feeback (RLHF) has become a key factor in aligning model behavior with users' goals. However, while humans integrate multiple strategies when making decisions, current RLHF approaches often simplify this process by modeling human reasoning through isolated tasks such as classification or regression. In this paper, we propose a novel reinforcement learning (RL) method that mimics human decision-making by jointly considering multiple tasks. Specifically, we lever…

Bayesian wavelet shrinkage for low SNR data based on the Epanechnikov kernel
Fidel Aniano Causil Barrios, Alex Rodrigo dos Santos Sousa
https://arxiv.org/abs/2507.11718

Bayesian wavelet shrinkage for low SNR data based on the Epanechnikov kernel
Consider the univariate nonparametric regression model with additive Gaussian noise and the representation of the unknown regression function in terms of a wavelet basis. We propose a shrinkage rule to estimate the wavelet coefficients obtained by mixing a point mass function at zero with the Epanechnikov distribution as a prior for the coefficients. The proposed rule proved to be suitable for application in scenarios with low signal-to-noise ratio datasets and outperformed standard and Bayesia…

Bayesian Double Machine Learning for Causal Inference
Francis J. DiTraglia, Laura Liu
https://arxiv.org/abs/2508.12688 https://arxiv.org/pdf/2508.12688

Bayesian Double Machine Learning for Causal Inference
This paper proposes a simple, novel, and fully-Bayesian approach for causal inference in partially linear models with high-dimensional control variables. Off-the-shelf machine learning methods can introduce biases in the causal parameter known as regularization-induced confounding. To address this, we propose a Bayesian Double Machine Learning (BDML) method, which modifies a standard Bayesian multivariate regression model and recovers the causal effect of interest from the reduced-form covarian…

FedCVD : Communication-Efficient Federated Learning for Cardiovascular Risk Prediction with Parametric and Non-Parametric Model Optimization
Abdelrhman Gaber, Hassan Abd-Eltawab, John Elgallab, Youssif Abuzied, Dineo Mpanya, Turgay Celik, Swarun Kumar, Tamer ElBatt
https://arxiv.org/abs/2507.22963…

FedCVD++: Communication-Efficient Federated Learning for Cardiovascular Risk Prediction with Parametric and Non-Parametric Model Optimization
Cardiovascular diseases (CVD) cause over 17 million deaths annually worldwide, highlighting the urgent need for privacy-preserving predictive systems. We introduce FedCVD++, an enhanced federated learning (FL) framework that integrates both parametric models (logistic regression, SVM, neural networks) and non-parametric models (Random Forest, XGBoost) for coronary heart disease risk prediction. To address key FL challenges, we propose: (1) tree-subset sampling that reduces Random Forest communi…

Generative Regression with IQ-BART
Sean O'Hagan, Veronika Ro\v{c}kov\'a
https://arxiv.org/abs/2507.04168 https://arxiv.org/pd…

Generative Regression with IQ-BART
Implicit Quantile BART (IQ-BART) posits a non-parametric Bayesian model on the conditional quantile function, acting as a model over a conditional model for $Y$ given $X$. One of the key ingredients is augmenting the observed data $\{(Y_i,X_i)\}_{i=1}^n$ with uniformly sampled values $τ_i$ for $1\leq i\leq n$ which serve as training data for quantile function estimation. Using the fact that the location parameter $μ$ in a $τ$-tilted asymmetric Laplace distribution corresponds to the $τ^{th}…

Quantile Reward Policy Optimization: Alignment with Pointwise Regression and Exact Partition Functions
Simon Matrenok, Skander Moalla, Caglar Gulcehre
https://arxiv.org/abs/2507.08068 https://arxiv.org/pdf/2507.08068 https://arxiv.org/html/2507.08068
arXiv:2507.08068v1 Announce Type: new
Abstract: Aligning large language models with pointwise absolute rewards has so far required online, on-policy algorithms such as PPO and GRPO. In contrast, simpler methods that can leverage offline or off-policy data, such as DPO and REBEL, are limited to learning from preference pairs or relative signals. To bridge this gap, we introduce \emph{Quantile Reward Policy Optimization} (QRPO), which learns from pointwise absolute rewards while preserving the simplicity and offline applicability of DPO-like methods. QRPO uses quantile rewards to enable regression to the closed-form solution of the KL-regularized RL objective. This reward yields an analytically tractable partition function, removing the need for relative signals to cancel this term. Moreover, QRPO scales with increased compute to estimate quantile rewards, opening a new dimension for pre-computation scaling. Empirically, QRPO consistently achieves top performance on chat and coding evaluations -- reward model scores, AlpacaEval 2, and LeetCode -- compared to DPO, REBEL, and SimPO across diverse datasets and 8B-scale models. Finally, we find that training with robust rewards instead of converting them to preferences induces less length bias.
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This https://arxiv.org/abs/2503.00772 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Bayesian inference for dynamic spatial quantile models with interactive effects
With the rapid advancement of information technology and data collection systems, large-scale spatial panel data presents new methodological and computational challenges. This paper introduces a dynamic spatial panel quantile model that incorporates unobserved heterogeneity. The proposed model captures the dynamic structure of panel data, high-dimensional cross-sectional dependence, and allows for heterogeneous regression coefficients. To estimate the model, we propose a novel Bayesian Markov C…

Soil Texture Prediction with Bayesian Generalized Additive Models for Spatial Compositional Data
Joaqu\'in Mart\'inez-Minaya, Lore Zumeta-Olaskoaga, Dae-Jin Lee
https://arxiv.org/abs/2508.07708

Soil Texture Prediction with Bayesian Generalized Additive Models for Spatial Compositional Data
Compositional data (CoDa) plays an important role in many fields such as ecology, geology, or biology. The most widely used modeling approaches are based on the Dirichlet and the logistic-normal formulation under Aitchison geometry. Recent developments in the mathematical field on the simplex geometry allow to express the regression model in terms of coordinates and estimate its coefficients. Once the model is projected in the real space, we can employ a multivariate Gaussian regression to deal…

Replaced article(s) found for econ.EM. https://arxiv.org/list/econ.EM/new
[1/1]:
- Nonparametric Treatment Effect Identification in School Choice
Jiafeng Chen
https://arxiv.org/abs/2112.03872
- Potential weights and implicit causal designs in linear regression
Jiafeng Chen
https://arxiv.org/abs/2407.21119 https://mastoxiv.page/@arXiv_econEM_bot/112885535340833628
- Reinterpreting demand estimation
Jiafeng Chen
https://arxiv.org/abs/2503.23524 https://mastoxiv.page/@arXiv_econEM_bot/114262944644769755
- A step towards the integration of machine learning and classic model-based survey methods
Tomasz \.Z\k{a}d{\l}o, Adam Chwila
https://arxiv.org/abs/2402.07521 https://mastoxiv.page/@arXiv_statME_bot/111924442633277025
- Galerkin-ARIMA: A Two-Stage Polynomial Regression Framework for Fast Rolling One-Step-Ahead Forec...
Haojie Liu, Zihan Lin
https://arxiv.org/abs/2507.07469 https://mastoxiv.page/@arXiv_statML_bot/114833708254543213
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AICO: Feature Significance Tests for Supervised Learning
Kay Giesecke, Enguerrand Horel, Chartsiri Jirachotkulthorn
https://arxiv.org/abs/2506.23396 https:…

AICO: Feature Significance Tests for Supervised Learning
The opacity of many supervised learning algorithms remains a key challenge, hindering scientific discovery and limiting broader deployment -- particularly in high-stakes domains. This paper develops model- and distribution-agnostic significance tests to assess the influence of input features in any regression or classification algorithm. Our method evaluates a feature's incremental contribution to model performance by masking its values across samples. Under the null hypothesis, the distributio…

Predictive Causal Inference via Spatio-Temporal Modeling and Penalized Empirical Likelihood
Byunghee Lee, Hye Yeon Sin, Joonsung Kang
https://arxiv.org/abs/2507.08896

Predictive Causal Inference via Spatio-Temporal Modeling and Penalized Empirical Likelihood
This study introduces an integrated framework for predictive causal inference designed to overcome limitations inherent in conventional single model approaches. Specifically, we combine a Hidden Markov Model (HMM) for spatial health state estimation with a Multi Task and Multi Graph Convolutional Network (MTGCN) for capturing temporal outcome trajectories. The framework asymmetrically treats temporal and spatial information regarding them as endogenous variables in the outcome regression, and e…

Structural Extrapolation in Regression Discontinuity Designs with an Application to School Expenditure Referenda
Austin Feng, Francesco Ruggieri
https://arxiv.org/abs/2508.02658

Structural Extrapolation in Regression Discontinuity Designs with an Application to School Expenditure Referenda
We propose a structural approach to extrapolate average partial effects away from the cutoff in regression discontinuity designs (RDDs). Our focus is on applications that exploit closely contested school district referenda to estimate the effects of changes in education spending on local economic outcomes. We embed these outcomes in a spatial equilibrium model of local jurisdictions in which fiscal policy is determined by majority rule voting. This integration provides a microfoundation for the…

A novel approach to generate distributions
Subhankar Dutta, Roberto Vila, Terezinha K. A. Ribeiro
https://arxiv.org/abs/2508.11861 https://arxiv.org/pdf/25…

A novel approach to generate distributions
A novel approach to adding two additional parameters to a family of distributions for better adaptability has been put forth. This approach yields a versatile class of distributions supported on the positive real line. We proceed to analyze its mathematical characteristics, such as critical points, modality, stochastic representation, identifiability, quantiles, moments, and truncated moments. We present a new regression model for unimodal continuous data based on a submodel of the newly propos…

Efficient Gibbs Sampling in Cox Regression Models Using Composite Partial Likelihood and P\'olya-Gamma Augmentation
Shu Tamano, Yui Tomo
https://arxiv.org/abs/2506.04675

Efficient Gibbs Sampling in Cox Regression Models Using Composite Partial Likelihood and Pólya-Gamma Augmentation
The Cox regression model and its Bayesian extensions are widely used in survival analysis. However, standard Bayesian approaches require modeling of the baseline hazard, and their full conditional distributions lack closed-form expressions. Therefore, the Metropolis-Hastings sampling algorithm is typically employed, whose efficiency is highly sensitive to the choice of proposal distribution. To address these issues, we propose the GS4Cox, an efficient Gibbs sampling algorithm for the Cox regres…

Partially-shared Imaging Regression on Integrating Heterogeneous Brain-Cognition Associations across Alzheimer's Diagnoses
Yang Sui, Qi Xu, Ting Li, Yang Bai, Annie Qu
https://arxiv.org/abs/2505.24259

Partially-shared Imaging Regression on Integrating Heterogeneous Brain-Cognition Associations across Alzheimer's Diagnoses
This paper is motivated by the heterogeneous associations among demographic covariates, imaging data, and cognitive performances across different diagnostic groups within the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We propose a novel PArtially-shared Imaging Regression (PAIR) model with smooth spatial component integration to capture heterogeneous imaging coefficients across multiple data sources. The model assumes that each imaging coefficient can be represented as a weighted…

This https://arxiv.org/abs/2310.16207 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Propensity weighting plus adjustment in proportional hazards model is not doubly robust
Recently, it has become common for applied works to combine commonly used survival analysis modeling methods, such as the multivariable Cox model and propensity score weighting, with the intention of forming a doubly robust estimator of an exposure effect hazard ratio that is unbiased in large samples when either the Cox model or the propensity score model is correctly specified. This combination does not, in general, produce a doubly robust estimator, even after regression standardization, whe…

This https://arxiv.org/abs/2504.14515 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

A robust mixed-effects quantile regression model using generalized Laplace mixtures to handle outliers and skewness
Mixed-effects quantile regression models are widely used to capture heterogeneous responses in hierarchically structured data. The asymmetric Laplace (AL) distribution has traditionally served as the basis for quantile regression; however, its fixed skewness limits flexibility and renders it sensitive to outliers. In contrast, the generalized asymmetric Laplace (GAL) distribution enables more flexible modeling of skewness and heavy-tailed behavior, yet it remains vulnerable to extreme observati…

Wavelet shrinkage based on the raised cosine prior
Juliana Marchesi Reina, Alex Rodrigo dos Santos Sousa
https://arxiv.org/abs/2507.10794 https://

Wavelet shrinkage based on the raised cosine prior
We propose a Bayesian shrinkage rule to estimate the wavelet coefficients in a nonparametric regression model with Gaussian errors, based on a mixture of a point mass function at zero and a symmetric, zero-centered raised cosine distribution prior. The proposed rule outperformed established shrinkage and thresholding methods in specific scenarios of signal-to-noise ratio and sample size values in conducted simulation studies involving the so-called Donoho and Johnstone test functions. Statistic…

Bayesian variable selection in a Cox proportional hazards model with the "Sum of Single Effects" prior
Yunqi Yang, Karl Tayeb, Peter Carbonetto, Xiaoyuan Zhong, Carole Ober, Matthew Stephens
https://arxiv.org/abs/2506.06233

Bayesian variable selection in a Cox proportional hazards model with the "Sum of Single Effects" prior
Motivated by genetic fine-mapping applications, we introduce a new approach to Bayesian variable selection regression (BVSR) for time-to-event (TTE) outcomes. This new approach is designed to deal with the specific challenges that arise in genetic fine-mapping, including: the presence of very strong correlations among the covariates, often exceeding 0.99; very large data sets containing potentially thousands of covariates and hundreds of thousands of samples. We accomplish this by extending the…

Inference in a generalized Bradley-Terry model for paired comparisons with covariates and a growing number of subjects
Ting Yan
https://arxiv.org/abs/2507.22472 https://

Inference in a generalized Bradley-Terry model for paired comparisons with covariates and a growing number of subjects
Motivated by the home-field advantage in sports, we propose a generalized Bradley--Terry model that incorporates covariate information for paired comparisons. It has an $n$-dimensional merit parameter $\bsβ$ and a fixed-dimensional regression coefficient $\bsγ$ for covariates. When the number of subjects $n$ approaches infinity and the number of comparisons between any two subjects is fixed, we show the uniform consistency of the maximum likelihood estimator (MLE) $(\widehat{\bsβ}, \widehat{…

Regression Analysis of Reciprocity in Directed Networks
Rui Feng, Chenlei Leng
https://arxiv.org/abs/2507.21469 https://arxiv.org/pdf/2507.21469

Regression Analysis of Reciprocity in Directed Networks
Reciprocity--the tendency of individuals to form mutual ties--is a fundamental structural feature of many directed networks. Despite its ubiquity, reciprocity remains insufficiently integrated into statistical network models, particularly in relation to covariate information. In this paper, we introduce the $R^{2}$-Model, a novel and flexible framework that explicitly models reciprocity while incorporating covariate effects. Built upon a generalized $p_1$ model, our framework accommodates both …

Stochastic Taylor expansion via Poisson point processes
Weichao Wu, Athanasios C. Micheas
https://arxiv.org/abs/2508.04703 https://arxiv.org/pdf/2508.04703…

Stochastic Taylor expansion via Poisson point processes
We generalize Taylor's theorem by introducing a stochastic formulation based on an underlying Poisson point process model. We utilize this approach to propose a novel non-linear regression framework and perform statistical inference of the model parameters. Theoretical properties of the proposed estimator are also proven, including its convergence, uniformly almost surely, to the true function. The theory is presented for the univariate and multivariate cases, and we exemplify the proposed meth…