Tootfinder

Cox Regression on the Plane
Yael Travis-Lumer, Micha Mandel, Rebecca A. Betensky, Malka Gorfine
https://arxiv.org/abs/2509.12473 https://arxiv.org/pdf/2509…

Cox Regression on the Plane
The Cox proportional hazards model is the most widely used regression model in univariate survival analysis. Extensions of the Cox model to bivariate survival data, however, remain scarce. We propose two novel extensions based on a Lehmann-type representation of the survival function. The first, the simple Lehmann model, is a direct extension that retains a straightforward structure. The second, the generalized Lehmann model, allows greater flexibility by incorporating three distinct regression…

Foundational theory for optimal decision tree problems. II. Optimal hypersurface decision tree algorithm
Xi He
https://arxiv.org/abs/2509.12057 https://arx…

Foundational theory for optimal decision tree problems. II. Optimal hypersurface decision tree algorithm
Decision trees are a ubiquitous model for classification and regression tasks due to their interpretability and efficiency. However, solving the optimal decision tree (ODT) problem remains a challenging combinatorial optimization task. Even for the simplest splitting rules--axis-parallel hyperplanes--it is NP-hard to optimize. In Part I of this series, we rigorously defined the proper decision tree model through four axioms and, based on these, introduced four formal definitions of the ODT prob…

Scale-Location-Truncated Beta Regression: Expanding Beta Regression to Accommodate 0 and 1
Mingang Kim, Brent A. Kaplan, Mikhail N. Koffarnus, Christopher T. Franck
https://arxiv.org/abs/2509.13167

Scale-Location-Truncated Beta Regression: Expanding Beta Regression to Accommodate 0 and 1
Beta regression is frequently used when the outcome variable y is bounded within a specific interval, transformed to the (0, 1) domain if necessary. However, standard beta regression cannot handle data observed at the boundary values of 0 or 1, as the likelihood function takes on values of either 0 or infinity. To address this issue, we propose the Scale-Location-Truncated beta (SLTB) regression model, which extends the beta distribution's domain to the [0, 1] interval. By using scale-location …

A High-Level Feature Model to Predict the Encoding Energy of a Hardware Video Encoder
Diwakara Reddy, Christian Herglotz, Andr\'e Kaup
https://arxiv.org/abs/2510.12754 https…

A High-Level Feature Model to Predict the Encoding Energy of a Hardware Video Encoder
In today's society, live video streaming and user generated content streamed from battery powered devices are ubiquitous. Live streaming requires real-time video encoding, and hardware video encoders are well suited for such an encoding task. In this paper, we introduce a high-level feature model using Gaussian process regression that can predict the encoding energy of a hardware video encoder. In an evaluation setup restricted to only P-frames and a single keyframe, the model can predict the e…

Scalable Variable Selection and Model Averaging for Latent Regression Models Using Approximate Variational Bayes
Gregor Zens, Mark F. J. Steel
https://arxiv.org/abs/2509.11751 h…

Scalable Variable Selection and Model Averaging for Latent Regression Models Using Approximate Variational Bayes
We propose a fast and theoretically grounded method for Bayesian variable selection and model averaging in latent variable regression models. Our framework addresses three interrelated challenges: (i) intractable marginal likelihoods, (ii) exponentially large model spaces, and (iii) computational costs in large samples. We introduce a novel integrated likelihood approximation based on mean-field variational posterior approximations and establish its asymptotic model selection consistency under …

A Type 2 Fuzzy Set Approach for Building Linear Linguistic Regression Analysis under Multi Uncertainty
Junzo Watada, Pei-Chun Lin, Bo Wang, Jeng-Shyang Pan, Jose Guadalupe Flores Muniz
https://arxiv.org/abs/2509.10498

A Type 2 Fuzzy Set Approach for Building Linear Linguistic Regression Analysis under Multi Uncertainty
In this paper, we propose a novel heuristic algorithm for constructing a Type-2 Fuzzy Set of the Linear Linguistic Regression (T2F-LLR) model, designed to address uncertainty and vagueness in real-world decision-making. We consider a practical scenario involving a cosmetic company's promotional planning across four product categories: Basic Face Care, Face Cleaning, Cosmetics, and Body Care, aimed at both male and female consumers. Data are collected using fuzzy linguistic questionnaires from c…

Varying Horizon Learning Economic MPC With Unknown Costs of Disturbed Nonlinear Systems
Weiliang Xiong, Defeng He, Haiping Du, Jianbin Mu
https://arxiv.org/abs/2509.11823 https:…

Varying Horizon Learning Economic MPC With Unknown Costs of Disturbed Nonlinear Systems
This paper proposes a novel varying horizon economic model predictive control (EMPC) scheme without terminal constraints for constrained nonlinear systems with additive disturbances and unknown economic costs. The general regression learning framework with mixed kernels is first used to reconstruct the unknown cost. Then an online iterative procedure is developed to adjust the horizon adaptively. Again, an elegant horizon-dependent contraction constraint is designed to ensure the convergence of…

Neural Scaling Laws for Deep Regression
Tilen Cadez, Kyoung-Min Kim
https://arxiv.org/abs/2509.10000 https://arxiv.org/pdf/2509.10000

Neural Scaling Laws for Deep Regression
Neural scaling laws--power-law relationships between generalization errors and characteristics of deep learning models--are vital tools for developing reliable models while managing limited resources. Although the success of large language models highlights the importance of these laws, their application to deep regression models remains largely unexplored. Here, we empirically investigate neural scaling laws in deep regression using a parameter estimation model for twisted van der Waals magnet…

Bayesian model updating via streamlined Bayesian active learning cubature
Pei-Pei Li, Chao Dang, Crist\'obal H. Acevedo, Marcos A. Valdebenito, Matthias G. R. Faes
https://arxiv.org/abs/2509.11204 …

Bayesian model updating via streamlined Bayesian active learning cubature
This paper proposes a novel Bayesian active learning method for Bayesian model updating, which is termed as "Streamlined Bayesian Active Learning Cubature" (SBALC). The core idea is to approximate the log-likelihood function using Gaussian process (GP) regression in a streamlined Bayesian active learning way. Rather than generating many samples from the posterior GP, we only use its mean and variance function to form the model evidence estimator, stopping criterion, and learning function. Speci…

Least squares-based methods to bias adjustment in scalar-on-function regression model using a functional instrumental variable
Xiwei Chen, Ufuk Beyaztas, Caihong Qin, Heyang Ji, Gilson Honvoh, Roger S. Zoh, Lan Xue, Carmen D. Tekwe
https://arxiv.org/abs/2509.12122

Least squares-based methods to bias adjustment in scalar-on-function regression model using a functional instrumental variable
Instrumental variables are widely used to adjust for measurement error bias when assessing associations of health outcomes with ME prone independent variables. IV approaches addressing ME in longitudinal models are well established, but few methods exist for functional regression. We develop two methods to adjust for ME bias in scalar on function linear models. We regress a scalar outcome on an ME prone functional variable using a functional IV for model identification and propose two least squ…

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…

L2-relaxation for Economic Prediction
Zhentao Shi, Yishu Wang
https://arxiv.org/abs/2510.12183 https://arxiv.org/pdf/2510.12183

L2-relaxation for Economic Prediction
We leverage an ensemble of many regressors, the number of which can exceed the sample size, for economic prediction. An underlying latent factor structure implies a dense regression model with highly correlated covariates. We propose the L2-relaxation method for estimating the regression coefficients and extrapolating the out-of-sample (OOS) outcomes. This framework can be applied to policy evaluation using the panel data approach (PDA), where we further establish inference for the average trea…

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…

Varying-Coefficient Fr\'echet Regression
Yanzhao Wang, Jianqiang Zhang, Wangli Xu
https://arxiv.org/abs/2509.11061 https://arxiv.org/pdf/2509.11061

Varying-Coefficient Fréchet Regression
As a growing number of problems involve variables that are random objects, the development of models for such data has become increasingly important. This paper introduces a novel varying-coefficient Fréchet regression model that extends the classical varying-coefficient framework to accommodate random objects as responses. The proposed model provides a unified methodology for analyzing both Euclidean and non-Euclidean response variables. We develop a comprehensive estimation procedure that ac…

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…

KOO Method-based Consistent Clustering for Group-wise Linear Regression with Graph Structure
M. Ohishi, R. Oda
https://arxiv.org/abs/2509.11103 https://arx…

KOO Method-based Consistent Clustering for Group-wise Linear Regression with Graph Structure
The kick-one-out (KOO) method is a variable selection method based on a model selection criterion. The method is very simple, and yet it has consistency in variable selection under a high-dimensional asymptotic framework with a specific model selection criterion. This paper proposes the join-twotogether (JTT) method, which is a clustering method based on the KOO method for group-wise linear regression with graph structure. The JTT method formulates the clustering problem as an edge selection pr…

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…

Learning-based model predictive control with moving horizon state estimation for autonomous racing
Yassine Kebbati, Andreas Rauh, Naima Ait-Oufroukh, Dalil Ichalal, Vincent Vigneron
https://arxiv.org/abs/2510.05366

Learning-based model predictive control with moving horizon state estimation for autonomous racing
This paper addresses autonomous racing by introducing a real-time nonlinear model predictive controller (NMPC) coupled with a moving horizon estimator (MHE). The racing problem is solved by an NMPC-based off-line trajectory planner that computes the best trajectory while considering the physical limits of the vehicle and circuit constraints. The developed controller is further enhanced with a learning extension based on Gaussian process regression that improves model predictions. The proposed c…

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 …

Bayesian Additive Regression Trees for functional ANOVA model
Seokhun Park, Insung Kong, Yongdai Kim
https://arxiv.org/abs/2509.03317 https://arxiv.org/pdf…

Bayesian Additive Regression Trees for functional ANOVA model
Bayesian Additive Regression Trees (BART) is a powerful statistical model that leverages the strengths of Bayesian inference and regression trees. It has received significant attention for capturing complex non-linear relationships and interactions among predictors. However, the accuracy of BART often comes at the cost of interpretability. To address this limitation, we propose ANOVA Bayesian Additive Regression Trees (ANOVA-BART), a novel extension of BART based on the functional ANOVA decompo…

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…

Robust Functional Logistic Regression
Berkay Akturk, Ufuk Beyaztas, Han Lin Shang
https://arxiv.org/abs/2510.12048 https://arxiv.org/pdf/2510.12048

Robust Functional Logistic Regression
Functional logistic regression is a popular model to capture a linear relationship between binary response and functional predictor variables. However, many methods used for parameter estimation in functional logistic regression are sensitive to outliers, which may lead to inaccurate parameter estimates and inferior classification accuracy. We propose a robust estimation procedure for functional logistic regression, in which the observations of the functional predictor are projected onto a set …

Self-learning QMC: application to the classical Holstein-Spin-Fermion model
Shaozhi Li
https://arxiv.org/abs/2509.05876 https://arxiv.org/pdf/2509.05876

Self-learning QMC: application to the classical Holstein-Spin-Fermion model
To evaluate the effectiveness of machine learning in systems with competing interactions, we developed a self-learning quantum Monte Carlo (SLQMC) method to simulate the phase transition in the classical Holstein-spin-fermion model. In SLQMC, machine learning techniques are employed to approximate the free energy, thereby bypassing the need for exact diagonalization and significantly reducing computational cost. We assess the performance of SLQMC using both linear regression and neural network …

Deducing Closed-Form Expressions for Bright-Solitons in Strongly Magnetized Plasmas with Physics Informed Symbolic Regression (PISR)
Edward Finkelstein
https://arxiv.org/abs/2510.02551

Deducing Closed-Form Expressions for Bright-Solitons in Strongly Magnetized Plasmas with Physics Informed Symbolic Regression (PISR)
This paper presents a novel approach to finding analytical approximations for bright-soliton solutions in strongly magnetized plasmas. We leverage Physics-Informed Symbolic Regression (PISR) to discover closed-form expressions for the vector potential and number density profiles, governed by a reduced-order model derived from Maxwell-fluid equations. The PISR framework combines symbolic regression with physics-based constraints, boundary conditions, and available simulation data to guide the se…

Optimized SVR Framework for Electric Load Forecasting
Nishant Gadde, Yoshua Alexander, Sarvesh Parthasarthy, Arman Allidina
https://arxiv.org/abs/2510.06476 https://

Optimized SVR Framework for Electric Load Forecasting
Load forecasting has always been a challenge for grid operators due to the growing complexity of power systems. The increase in extreme weather and the need for energy from customers has led to load forecasting sometimes failing. This research presents a Support Vector Regression (SVR) framework for electric load forecasting that outperforms the industry standard. The SVR model demonstrates better accuracy across all evaluation metrics that are important for power system operations. The model h…

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…

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…

Panel regression for the GDP of the Central and Eastern European countries using time-varying coefficients
Lesya Kolinets, Vygintas Gontis
https://arxiv.org/abs/2510.04211 https…

Panel regression for the GDP of the Central and Eastern European countries using time-varying coefficients
The integration of Central and Eastern European (CEE) countries into the European Economic Area serves as a valuable experiment for the regional economic development theory. The long-lasting convergence of these economies with more advanced Western Europe exhibits a few standard features and varying policies implemented. Even the Baltic countries, which started from very similar starting positions, demonstrate their unique trajectories of development. We employ a panel data regression model tha…

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…

Assessing the Influence of Locational Suitability on the Spatial Distribution of Household Wealth in Bernalillo County, NM
Onyedikachi J. Okeke, Uloma E. Nelson, Chukwudi Nwaogu, Olumide O. Oladoyin, Emmanuel Kubuafor, Dennis Baidoo, Titilope Akinyemi, Adedoyin S. Ajeyomi, Rekiya A. Idris, Isaac A. Fabunmi
https://arxiv.org/abs/2510.11048

Assessing the Influence of Locational Suitability on the Spatial Distribution of Household Wealth in Bernalillo County, NM
This study applies Multiscale Geographically Weighted Regression (MGWR) to examine the spatial determinants of household wealth in Bernalillo County, New Mexico. The model incorporates sociodemographic, environmental, and proximity-based variables to evaluate how locational suitability influences economic outcomes. Key factors considered include income, home value, elevation, PM2.5 concentration, and distances to essential services such as schools, markets, and hospitals. The MGWR model demonst…

On function-on-function linear quantile regression
Muge Mutis, Ufuk Beyaztas, Filiz Karaman, Han Lin Shang
https://arxiv.org/abs/2510.10792 https://arxiv.o…

On function-on-function linear quantile regression
We present two innovative functional partial quantile regression algorithms designed to accurately and efficiently estimate the regression coefficient function within the function-on-function linear quantile regression model. Our algorithms utilize functional partial quantile regression decomposition to effectively project the infinite-dimensional response and predictor variables onto a finite-dimensional space. Within this framework, the partial quantile regression components are approximated …

Leveraging Support Vector Regression for Outcome Prediction in Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy
Yajun Yu, Steve Jiang, Robert Timmerman, Hao Peng
https://arxiv.org/abs/2509.07872

Leveraging Support Vector Regression for Outcome Prediction in Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy
Personalized ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) is a novel treatment that delivers radiation in pulses of protracted intervals. Accurate prediction of gross tumor volume (GTV) changes through regression models has substantial prognostic value. This study aims to develop a multi-omics based support vector regression (SVR) model for predicting GTV change. A retrospective cohort of 39 patients with 69 brain metastases was analyzed, based on radiomics (MRI images) and do…

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…

Identifying Neural Signatures from fMRI using Hybrid Principal Components Regression
Jared Rieck, Julia Wrobel, Joshua L. Gowin, Yue Wang, Martin Paulus, Ryan Peterson
https://arxiv.org/abs/2509.07300 …

Identifying Neural Signatures from fMRI using Hybrid Principal Components Regression
Recent advances in neuroimaging analysis have enabled accurate decoding of mental state from brain activation patterns during functional magnetic resonance imaging scans. A commonly applied tool for this purpose is principal components regression regularized with the least absolute shrinkage and selection operator (LASSO PCR), a type of multi-voxel pattern analysis (MVPA). This model presumes that all components are equally likely to harbor relevant information, when in fact the task-related si…

Regression Language Models for Code
Yash Akhauri, Xingyou Song, Arissa Wongpanich, Bryan Lewandowski, Mohamed S. Abdelfattah
https://arxiv.org/abs/2509.26476 https://

Regression Language Models for Code
We study code-to-metric regression: predicting numeric outcomes of code executions, a challenging task due to the open-ended nature of programming languages. While prior methods have resorted to heavy and domain-specific feature engineering, we show that a single unified Regression Language Model (RLM) can simultaneously predict directly from text, (i) the memory footprint of code across multiple high-level languages such as Python and C++, (ii) the latency of Triton GPU kernels, and (iii) the …

Artificial Intelligence for Cost-Aware Resource Prediction in Big Data Pipelines
Harshit Goyal
https://arxiv.org/abs/2510.05127 https://arxiv.org/pdf/2510.…

Artificial Intelligence for Cost-Aware Resource Prediction in Big Data Pipelines
Efficient resource allocation is a key challenge in modern cloud computing. Over-provisioning leads to unnecessary costs, while under-provisioning risks performance degradation and SLA violations. This work presents an artificial intelligence approach to predict resource utilization in big data pipelines using Random Forest regression. We preprocess the Google Borg cluster traces to clean, transform, and extract relevant features (CPU, memory, usage distributions). The model achieves high predi…

YOLO-Count: Differentiable Object Counting for Text-to-Image Generation
Guanning Zeng, Xiang Zhang, Zirui Wang, Haiyang Xu, Zeyuan Chen, Bingnan Li, Zhuowen Tu
https://arxiv.org/abs/2508.00728

YOLO-Count: Differentiable Object Counting for Text-to-Image Generation
We propose YOLO-Count, a differentiable open-vocabulary object counting model that tackles both general counting challenges and enables precise quantity control for text-to-image (T2I) generation. A core contribution is the 'cardinality' map, a novel regression target that accounts for variations in object size and spatial distribution. Leveraging representation alignment and a hybrid strong-weak supervision scheme, YOLO-Count bridges the gap between open-vocabulary counting and T2I generation …

Learning Linear Regression with Low-Rank Tasks in-Context
Kaito Takanami, Takashi Takahashi, Yoshiyuki Kabashima
https://arxiv.org/abs/2510.04548 https://a…

Learning Linear Regression with Low-Rank Tasks in-Context
In-context learning (ICL) is a key building block of modern large language models, yet its theoretical mechanisms remain poorly understood. It is particularly mysterious how ICL operates in real-world applications where tasks have a common structure. In this work, we address this problem by analyzing a linear attention model trained on low-rank regression tasks. Within this setting, we precisely characterize the distribution of predictions and the generalization error in the high-dimensional li…

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

(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…

Linear Regression under Missing or Corrupted Coordinates
Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane, Jasper C. H. Lee, Thanasis Pittas
https://arxiv.org/abs/2509.19242

Sparse Convex Quantile Regression: A Generalized Benders Decomposition Approach
Xiaoyu Luo, Chuanhou Gao
https://arxiv.org/abs/2509.01936 https://arxiv.org…

Sparse Convex Quantile Regression: A Generalized Benders Decomposition Approach
We develop a scalable algorithmic framework for sparse convex quantile regression (SCQR), addressing key computational challenges in the literature. Enhancing the classical CQR model, we introduce L2-norm regularization and an epsilon-insensitive zone to improve generalization and mitigate overfitting - both theoretically justified and empirically validated. Based on this extension, we improve the SCQR model and propose the first Generalized Benders Decomposition (GBD) algorithm tailored to thi…

MASt3R-Fusion: Integrating Feed-Forward Visual Model with IMU, GNSS for High-Functionality SLAM
Yuxuan Zhou, Xingxing Li, Shengyu Li, Zhuohao Yan, Chunxi Xia, Shaoquan Feng
https://arxiv.org/abs/2509.20757

MASt3R-Fusion: Integrating Feed-Forward Visual Model with IMU, GNSS for High-Functionality SLAM
Visual SLAM is a cornerstone technique in robotics, autonomous driving and extended reality (XR), yet classical systems often struggle with low-texture environments, scale ambiguity, and degraded performance under challenging visual conditions. Recent advancements in feed-forward neural network-based pointmap regression have demonstrated the potential to recover high-fidelity 3D scene geometry directly from images, leveraging learned spatial priors to overcome limitations of traditional multi-v…

Probing redshift-dependent systematics in the Hubble tension: Model-independent $H_0$ constraints from DESI R2
Tonghua Liu, Shuo Cao, Jieci Wang
https://arxiv.org/abs/2509.20898

Probing redshift-dependent systematics in the Hubble tension: Model-independent $H_0$ constraints from DESI R2
We present a model-independent determination of the Hubble constant ($H_0$) using the latest observational data from multiple cosmological probes. By combining baryon acoustic oscillation (BAO) measurements from the second data release of the Dark Energy Spectroscopic Instrument (DESI DR2), cosmic chronometer $H(z)$ data, and the Pantheon Plus Type Ia supernova (SN Ia) sample, we reconstruct the cosmic expansion history through Gaussian process regression without assuming a specific cosmologica…

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…

An Adaptive Multi Agent Bitcoin Trading System
Aadi Singhi
https://arxiv.org/abs/2510.08068 https://arxiv.org/pdf/2510.08068…

An Adaptive Multi Agent Bitcoin Trading System
This paper presents a Multi Agent Bitcoin Trading system that utilizes Large Lan- guage Models (LLMs) for alpha generation and portfolio management in the cryptocur- rencies market. Unlike equities, cryptocurrencies exhibit extreme volatility and are heavily influenced by rapidly shifting market sentiments and regulatory announcements, making them difficult to model using static regression models or neural networks trained solely on historical data [53]. The proposed framework overcomes this by…

Preventing Model Collapse Under Overparametrization: Optimal Mixing Ratios for Interpolation Learning and Ridge Regression
Anvit Garg, Sohom Bhattacharya, Pragya Sur
https://arxiv.org/abs/2509.22341

Preventing Model Collapse Under Overparametrization: Optimal Mixing Ratios for Interpolation Learning and Ridge Regression
Model collapse occurs when generative models degrade after repeatedly training on their own synthetic outputs. We study this effect in overparameterized linear regression in a setting where each iteration mixes fresh real labels with synthetic labels drawn from the model fitted in the previous iteration. We derive precise generalization error formulae for minimum-$\ell_2$-norm interpolation and ridge regression under this iterative scheme. Our analysis reveals intriguing properties of the optim…

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…

Automated Constitutive Model Discovery by Pairing Sparse Regression Algorithms with Model Selection Criteria
Jorge-Humberto Urrea-Quintero, David Anton, Laura De Lorenzis, Henning Wessels
https://arxiv.org/abs/2509.16040

Automated Constitutive Model Discovery by Pairing Sparse Regression Algorithms with Model Selection Criteria
The automated discovery of constitutive models from data has recently emerged as a promising alternative to the traditional model calibration paradigm. In this work, we present a fully automated framework for constitutive model discovery that systematically pairs three sparse regression algorithms (Least Absolute Shrinkage and Selection Operator (LASSO), Least Angle Regression (LARS), and Orthogonal Matching Pursuit (OMP)) with three model selection criteria: $K$-fold cross-validation (CV), Aka…

Conformal prediction without knowledge of labeled calibration data
Jonas Flechsig, Maximilian Pilz
https://arxiv.org/abs/2509.10321 https://arxiv.org/pdf/2…

Conformal prediction without knowledge of labeled calibration data
We extend the method of conformal prediction beyond the case relying on labeled calibration data. Replacing the calibration scores by suitable estimates, we identify conformity sets $C$ for classification and regression models that rely on unlabeled calibration data. Given a classification model with accuracy $1-β$, we prove that the conformity sets guarantee a coverage of $P(Y \in C) \geq 1-α-β$ for an arbitrary parameter $α\in (0,1)$. The same coverage guarantee also holds for regression …

Modeling Spatio-Temporal Dynamics of Obesity in Italian Regions Via Bayesian Beta Regression
Luciano Rota, Raffaele Argiento, Michela Cameletti
https://arxiv.org/abs/2508.05719 …

Modeling Spatio-Temporal Dynamics of Obesity in Italian Regions Via Bayesian Beta Regression
In this paper we investigate the spatio-temporal dynamics of obesity rates across Italian regions from 2010 to 2022, aiming to identify spatial and temporal trends and assess potential heterogeneities. We implement a Bayesian hierarchical Beta regression model to analyze regional obesity rates, integrating spatial and temporal random effects, alongside gender and various exogenous predictors. The model leverages the Stochastic Search Variable Selection technique to identify significant predicto…

Autoencoder-based non-intrusive model order reduction in continuum mechanics
Jannick Kehls, Ellen Kuhl, Tim Brepols, Kevin Linka, Hagen Holthusen
https://arxiv.org/abs/2509.02237

Autoencoder-based non-intrusive model order reduction in continuum mechanics
We propose a non-intrusive, Autoencoder-based framework for reduced-order modeling in continuum mechanics. Our method integrates three stages: (i) an unsupervised Autoencoder compresses high-dimensional finite element solutions into a compact latent space, (ii) a supervised regression network maps problem parameters to latent codes, and (iii) an end-to-end surrogate reconstructs full-field solutions directly from input parameters. To overcome limitations of existing approaches, we propose two…

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…

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…

Fisher Information, Training and Bias in Fourier Regression Models
Lorenzo Pastori, Veronika Eyring, Mierk Schwabe
https://arxiv.org/abs/2510.06945 https://

Fisher Information, Training and Bias in Fourier Regression Models
Motivated by the growing interest in quantum machine learning, in particular quantum neural networks (QNNs), we study how recently introduced evaluation metrics based on the Fisher information matrix (FIM) are effective for predicting their training and prediction performance. We exploit the equivalence between a broad class of QNNs and Fourier models, and study the interplay between the \emph{effective dimension} and the \emph{bias} of a model towards a given task, investigating how these affe…

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…

Complete Gaussian Splats from a Single Image with Denoising Diffusion Models
Ziwei Liao, Mohamed Sayed, Steven L. Waslander, Sara Vicente, Daniyar Turmukhambetov, Michael Firman
https://arxiv.org/abs/2508.21542

Complete Gaussian Splats from a Single Image with Denoising Diffusion Models
Gaussian splatting typically requires dense observations of the scene and can fail to reconstruct occluded and unobserved areas. We propose a latent diffusion model to reconstruct a complete 3D scene with Gaussian splats, including the occluded parts, from only a single image during inference. Completing the unobserved surfaces of a scene is challenging due to the ambiguity of the plausible surfaces. Conventional methods use a regression-based formulation to predict a single "mode" for occluded…

Learning Multi-Index Models with Hyper-Kernel Ridge Regression
Shuo Huang, Hippolyte Labarri\`ere, Ernesto De Vito, Tomaso Poggio, Lorenzo Rosasco
https://arxiv.org/abs/2510.02532

Learning Multi-Index Models with Hyper-Kernel Ridge Regression
Deep neural networks excel in high-dimensional problems, outperforming models such as kernel methods, which suffer from the curse of dimensionality. However, the theoretical foundations of this success remain poorly understood. We follow the idea that the compositional structure of the learning task is the key factor determining when deep networks outperform other approaches. Taking a step towards formalizing this idea, we consider a simple compositional model, namely the multi-index model (MIM…

Functional Regression with Nonstationarity and Error Contamination: Application to the Economic Impact of Climate Change
Kyungsik Nam, Won-Ki Seo
https://arxiv.org/abs/2509.08591

Functional Regression with Nonstationarity and Error Contamination: Application to the Economic Impact of Climate Change
This paper studies a functional regression model with nonstationary dependent and explanatory functional observations, in which the nonstationary stochastic trends of the dependent variable are explained by those of the explanatory variable, and the functional observations may be error-contaminated. We develop novel autocovariance-based estimation and inference methods for this model. The methodology is broadly applicable to economic and statistical functional time series with nonstationary dyn…

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…

Uncertainty Quantification for Regression using Proper Scoring Rules
Alexander Fishkov, Kajetan Schweighofer, Mykyta Ielanskyi, Nikita Kotelevskii, Mohsen Guizani, Maxim Panov
https://arxiv.org/abs/2509.26610

Uncertainty Quantification for Regression using Proper Scoring Rules
Quantifying uncertainty of machine learning model predictions is essential for reliable decision-making, especially in safety-critical applications. Recently, uncertainty quantification (UQ) theory has advanced significantly, building on a firm basis of learning with proper scoring rules. However, these advances were focused on classification, while extending these ideas to regression remains challenging. In this work, we introduce a unified UQ framework for regression based on proper scoring r…

Beyond the Oracle Property: Adaptive LASSO in Cointegrating Regressions
Karsten Reichold, Ulrike Schneider
https://arxiv.org/abs/2510.07204 https://arxiv.o…

Beyond the Oracle Property: Adaptive LASSO in Cointegrating Regressions
This paper establishes new asymptotic results for the adaptive LASSO estimator in cointegrating regression models. We study model selection probabilities, estimator consistency, and limiting distributions under both standard and moving-parameter asymptotics. We also derive uniform convergence rates and the fastest local-to-zero rates that can still be detected by the estimator, complementing and extending the results of Lee, Shi, and Gao (2022, Journal of Econometrics, 229, 322--349). Our main …

Adaptive Market Intelligence: A Mixture of Experts Framework for Volatility-Sensitive Stock Forecasting
Diego Vallarino
https://arxiv.org/abs/2508.02686 https://

Adaptive Market Intelligence: A Mixture of Experts Framework for Volatility-Sensitive Stock Forecasting
This study develops and empirically validates a Mixture of Experts (MoE) framework for stock price prediction across heterogeneous volatility regimes using real market data. The proposed model combines a Recurrent Neural Network (RNN) optimized for high-volatility stocks with a linear regression model tailored to stable equities. A volatility-aware gating mechanism dynamically weights the contributions of each expert based on asset classification. Using a dataset of 30 publicly traded U.S. stoc…

Total Robustness in Bayesian Nonlinear Regression for Measurement Error Problems under Model Misspecification
Mengqi Chen, Charita Dellaporta, Thomas B. Berrett, Theodoros Damoulas
https://arxiv.org/abs/2510.03131

Total Robustness in Bayesian Nonlinear Regression for Measurement Error Problems under Model Misspecification
Modern regression analyses are often undermined by covariate measurement error, misspecification of the regression model, and misspecification of the measurement error distribution. We present, to the best of our knowledge, the first Bayesian nonparametric framework targeting total robustness that tackles all three challenges in general nonlinear regression. The framework assigns a Dirichlet process prior to the latent covariate-response distribution and updates it with posterior pseudo-samples…

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…

Uncertainty Quantification for Multi-level Models Using the Survey-Weighted Pseudo-Posterior
Matthew R. Williams, F. Hunter McGuire, Terrance D. Savitsky
https://arxiv.org/abs/2510.09401

Uncertainty Quantification for Multi-level Models Using the Survey-Weighted Pseudo-Posterior
Parameter estimation and inference from complex survey samples typically focuses on global model parameters whose estimators have asymptotic properties, such as from fixed effects regression models. We present a motivating example of Bayesian inference for a multi-level or mixed effects model in which both the local parameters (e.g. group level random effects) and the global parameters may need to be adjusted for the complex sampling design. We evaluate the limitations of the survey-weighted ps…

Theory of Scaling Laws for In-Context Regression: Depth, Width, Context and Time
Blake Bordelon, Mary I. Letey, Cengiz Pehlevan
https://arxiv.org/abs/2510.01098 https://

Theory of Scaling Laws for In-Context Regression: Depth, Width, Context and Time
We study in-context learning (ICL) of linear regression in a deep linear self-attention model, characterizing how performance depends on various computational and statistical resources (width, depth, number of training steps, batch size and data per context). In a joint limit where data dimension, context length, and residual stream width scale proportionally, we analyze the limiting asymptotics for three ICL settings: (1) isotropic covariates and tasks (ISO), (2) fixed and structured covarianc…

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…

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…

L3Cube-MahaSTS: A Marathi Sentence Similarity Dataset and Models
Aishwarya Mirashi, Ananya Joshi, Raviraj Joshi
https://arxiv.org/abs/2508.21569 https://ar…

L3Cube-MahaSTS: A Marathi Sentence Similarity Dataset and Models
We present MahaSTS, a human-annotated Sentence Textual Similarity (STS) dataset for Marathi, along with MahaSBERT-STS-v2, a fine-tuned Sentence-BERT model optimized for regression-based similarity scoring. The MahaSTS dataset consists of 16,860 Marathi sentence pairs labeled with continuous similarity scores in the range of 0-5. To ensure balanced supervision, the dataset is uniformly distributed across six score-based buckets spanning the full 0-5 range, thus reducing label bias and enhancing …

High-Dimensional Matrix-Variate Diffusion Index Models for Time Series Forecasting
Zhiren Ma, Qian Zhao, Riquan Zhang, Zhaoxing Gao
https://arxiv.org/abs/2508.04259 https://

High-Dimensional Matrix-Variate Diffusion Index Models for Time Series Forecasting
This paper proposes a novel diffusion-index model for forecasting when predictors are high-dimensional matrix-valued time series. We apply an $α$-PCA method to extract low-dimensional matrix factors and build a bilinear regression linking future outcomes to these factors, estimated via iterative least squares. To handle weak factor structures, we introduce a supervised screening step to select informative rows and columns. Theoretical properties, including consistency and asymptotic normality,…

Semiparametric model averaging for high-dimensional quantile regression with nonignorable nonresponse
Wei Xiong, Dianliang Deng, Dehui Wang
https://arxiv.org/abs/2509.00464 http…

Semiparametric model averaging for high-dimensional quantile regression with nonignorable nonresponse
Model averaging has demonstrated superior performance for ensemble forecasting in high-dimensional framework, its extension to incomplete datasets remains a critical but underexplored challenge. Moreover, identifying the parsimonious model through averaging procedure in quantile regression demands urgent methodological innovation. In this paper, we propose a novel model averaging method for high-dimensional quantile regression with nonignorable missingness. The idea is to relax the parametric c…

What is in the model? A Comparison of variable selection criteria and model search approaches
Shuangshuang Xu, Marco A. R. Ferreira, Allison N. Tegge
https://arxiv.org/abs/2510.02628

What is in the model? A Comparison of variable selection criteria and model search approaches
For many scientific questions, understanding the underlying mechanism is the goal. To help investigators better understand the underlying mechanism, variable selection is a crucial step that permits the identification of the most associated regression variables of interest. A variable selection method consists of model evaluation using an information criterion and a search of the model space. Here, we provide a comprehensive comparison of variable selection methods using performance measures of…

Towards a Certificate of Trust: Task-Aware OOD Detection for Scientific AI
Bogdan Raoni\'c, Siddhartha Mishra, Samuel Lanthaler
https://arxiv.org/abs/2509.25080 https://

Towards a Certificate of Trust: Task-Aware OOD Detection for Scientific AI
Data-driven models are increasingly adopted in critical scientific fields like weather forecasting and fluid dynamics. These methods can fail on out-of-distribution (OOD) data, but detecting such failures in regression tasks is an open challenge. We propose a new OOD detection method based on estimating joint likelihoods using a score-based diffusion model. This approach considers not just the input but also the regression model's prediction, providing a task-aware reliability score. Across num…

Inference in pseudo-observation-based regression using (biased) covariance estimation and naive bootstrapping
Simon Mack, Morten Overgaard, Dennis Dobler
https://arxiv.org/abs/2510.06815

Inference in pseudo-observation-based regression using (biased) covariance estimation and naive bootstrapping
We demonstrate that the usual Huber-White estimator is not consistent for the limiting covariance of parameter estimates in pseudo-observation regression approaches. By confirming that a plug-in estimator can be used instead, we obtain asymptotically exact and consistent tests for general linear hypotheses in the parameters of the model. Additionally, we confirm that naive bootstrapping can not be used for covariance estimation in the pseudo-observation model either. However, it can be used for…

FBMS: An R Package for Flexible Bayesian Model Selection and Model Averaging
Florian Frommlet, Jon Lachmann, Geir Storvik, Aliaksandr Hubin
https://arxiv.org/abs/2509.00753 http…

FBMS: An R Package for Flexible Bayesian Model Selection and Model Averaging
The FBMS R package facilitates Bayesian model selection and model averaging in complex regression settings by employing a variety of Monte Carlo model exploration methods. At its core, the package implements an efficient Mode Jumping Markov Chain Monte Carlo (MJMCMC) algorithm, designed to improve mixing in multi-modal posterior landscapes within Bayesian generalized linear models. In addition, it provides a genetically modified MJMCMC (GMJMCMC) algorithm that introduces nonlinear feature gener…

Fitting sparse high-dimensional varying-coefficient models with Bayesian regression tree ensembles
Soham Ghosh, Saloni Bhogale, Sameer K. Deshpande
https://arxiv.org/abs/2510.08204

Fitting sparse high-dimensional varying-coefficient models with Bayesian regression tree ensembles
By allowing the effects of $p$ covariates in a linear regression model to vary as functions of $R$ additional effect modifiers, varying-coefficient models (VCMs) strike a compelling balance between interpretable-but-rigid parametric models popular in classical statistics and flexible-but-opaque methods popular in machine learning. But in high-dimensional settings where $p$ and/or $R$ exceed the number of observations, existing approaches to fitting VCMs fail to identify which covariates have a …

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…

Regression for spherical responses with linear and spherical covariates using a scaled link function
Shogo Kato, Kassel L. Hingee, Janice L. Scealy, Andrew T. A. Wood
https://arxiv.org/abs/2509.06204

Regression for spherical responses with linear and spherical covariates using a scaled link function
We propose a regression model in which the responses are spherical variables and the covariates include linear and/or spherical variables. A novel link function is introduced by extending the Möbius transformation on the sphere. This link function is an anisotropic mapping that enables scale control along each axis of the spherical covariates and for each linear covariate. It generalizes several well-known link functions for circular or linear covariates. Each parameter of the link function is…

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…

A more interpretable regression model for count data with excess of zeros
Gustavo H. A. Pereira, Jeremias Le\~ao, Manoel Santos-Neto, Jianwen Cai
https://arxiv.org/abs/2509.24916

A more interpretable regression model for count data with excess of zeros
Count data are common in medical research. When these data have more zeros than expected by the most used count distributions, it is common to employ a zero-inflated regression model. However, the interpretability of these models is much lower than the most used count regression models. In this work, we introduce a more interpretable regression model for count data with excess of zeros based on a reparameterization of the zero-inflated Poisson distribution. We discuss inferential and diagnostic…

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…

Predicting Social Media Engagement from Emotional and Temporal Features
Yunwoo Kim, Junhyuk Hwang
https://arxiv.org/abs/2508.21650 https://arxiv.org/pdf/25…

Predicting Social Media Engagement from Emotional and Temporal Features
We present a machine learning approach for predicting social media engagement (comments and likes) from emotional and temporal features. The dataset contains 600 songs with annotations for valence, arousal, and related sentiment metrics. A multi target regression model based on HistGradientBoostingRegressor is trained on log transformed engagement ratios to address skewed targets. Performance is evaluated with both a custom order of magnitude accuracy and standard regression metrics, including …

Posterior Summarization for Variable Selection in Bayesian Tree Ensembles
Shengbin Ye, Meng Li
https://arxiv.org/abs/2509.07121 https://arxiv.org/pdf/2509.…

Posterior Summarization for Variable Selection in Bayesian Tree Ensembles
Variable selection remains a fundamental challenge in statistics, especially in nonparametric settings where model complexity can obscure interpretability. Bayesian tree ensembles, particularly the popular Bayesian additive regression trees (BART) and their rich variants, offer strong predictive performance with interpretable variable importance measures. We modularize Bayesian tree ensemble methods into model choice (tree prior) and posterior summary, and provide a unified review of existing s…

Sparse Seemingly Unrelated Regression (SSUR) Copula Mixed Models for Multivariate Loss Reserving
Pengfei Cai, Anas Abdallah, Pratheepa Jeganathan
https://arxiv.org/abs/2509.05426

Sparse Seemingly Unrelated Regression (SSUR) Copula Mixed Models for Multivariate Loss Reserving
Insurance companies often operate across multiple interrelated lines of business (LOBs), and accounting for dependencies between them is essential for accurate reserve estimation and risk capital determination. In our previous work on the Extended Deep Triangle (EDT), we demonstrated that a more flexible model that uses multiple companies' data reduces reserve prediction error and increases diversification benefits. However, the EDT's limitation lies in its limited interpretability of the depen…

A Compositional Kernel Model for Feature Learning
Feng Ruan, Keli Liu, Michael Jordan
https://arxiv.org/abs/2509.14158 https://arxiv.org/pdf/2509.14158

A Compositional Kernel Model for Feature Learning
We study a compositional variant of kernel ridge regression in which the predictor is applied to a coordinate-wise reweighting of the inputs. Formulated as a variational problem, this model provides a simple testbed for feature learning in compositional architectures. From the perspective of variable selection, we show how relevant variables are recovered while noise variables are eliminated. We establish guarantees showing that both global minimizers and stationary points discard noise coordin…

Repro Samples Method for Model-Free Inference in High-Dimensional Binary Classification
Xiaotian Hou, Peng Wang, Minge Xie, Linjun Zhang
https://arxiv.org/abs/2510.01468 https:/…

Repro Samples Method for Model-Free Inference in High-Dimensional Binary Classification
This paper presents a novel method for statistical inference in high-dimensional binary models with unspecified structure, where we leverage a (potentially misspecified) sparsity-constrained working generalized linear model (GLM) to facilitate the inference process. Our method is based on the repro samples framework, which generates artificial samples that mimic the actual data-generating process. Our inference targets include the model support, case probabilities, and the oracle regression coe…

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…

Physics-Informed Spectral Modeling for Hyperspectral Imaging
Zuzanna Gawrysiak, Krzysztof Krawiec
https://arxiv.org/abs/2508.21618 https://arxiv.org/pdf/25…

Physics-Informed Spectral Modeling for Hyperspectral Imaging
We present PhISM, a physics-informed deep learning architecture that learns without supervision to explicitly disentangle hyperspectral observations and model them with continuous basis functions. \mname outperforms prior methods on several classification and regression benchmarks, requires limited labeled data, and provides additional insights thanks to interpretable latent representation.

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…

Local aggregate multiscale processes: A scalable, machine-learning-compatible spatial model
Daisuke Murakami, Alexis Comber, Takahiro Yoshida, Narumasa Tsutsumida, Chris Brunsdon, Tomoki Nakaya
https://arxiv.org/abs/2510.00968

Local aggregate multiscale processes: A scalable, machine-learning-compatible spatial model
This study develops the Local Aggregate Multiscale Process (LAMP), a scalable and machine-learning-compatible alternative to conventional spatial Gaussian processes (GPs, or kriging). Unlike conventional covariance-based spatial models, LAMP represents spatial processes by a multiscale ensemble of local models, inspired by geographically weighted regression. To ensure stable model training, larger-scale patterns that are easier to learn are modeled first, followed by smaller-scale patterns, wit…

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{…

Some Simplifications for the Expectation-Maximization (EM) Algorithm: The Linear Regression Model Case
Daniel A. Griffith
https://arxiv.org/abs/2509.19461 https://

Some Simplifications for the Expectation-Maximization (EM) Algorithm: The Linear Regression Model Case
The EM algorithm is a generic tool that offers maximum likelihood solutions when datasets are incomplete with data values missing at random or completely at random. At least for its simplest form, the algorithm can be rewritten in terms of an ANCOVA regression specification. This formulation allows several analytical results to be derived that permit the EM algorithm solution to be expressed in terms of new observation predictions and their variances. Implementations can be made with a linear r…

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…

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 …

Interpretable Scalar-on-Image Linear Regression Models via the Generalized Dantzig Selector
Sijia Liao, Xiaoxiao Sun, Ning Hao, Hao Helen Zhang
https://arxiv.org/abs/2508.20278 …

Interpretable Scalar-on-Image Linear Regression Models via the Generalized Dantzig Selector
The scalar-on-image regression model examines the association between a scalar response and a bivariate function (e.g., images) through the estimation of a bivariate coefficient function. Existing approaches often impose smoothness constraints to control the bias-variance trade-off, and thus prevent overfitting. However, such assumptions can hinder interpretability, especially when only certain regions of an image influence changes in the response. In such a scenario, interpretability can be be…

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…

Analyzing health care data using count models: A novel approach to Length of Stay analysis
Peer Bilal Ahmad, Na Elah
https://arxiv.org/abs/2509.02703 https://

Analyzing health care data using count models: A novel approach to Length of Stay analysis
Count data modeling has been extensively applied in medical sciences to analyze various healthcare datasets. Numerous probability models have been developed to address diverse aspects of healthcare data. In this study, we propose a novel count data model for analyzing healthcare datasets. Key structural properties of the model are established, and an associated regression framework is introduced to examine the effects of various covariates. Additionally, a three-inflated distribution, based on …

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…

Bridging Control Variates and Regression Adjustment in A/B Testing: From Design-Based to Model-Based Frameworks
Yu Zhang, Bokui Wan, Yongli Qin
https://arxiv.org/abs/2509.13944 …

Bridging Control Variates and Regression Adjustment in A/B Testing: From Design-Based to Model-Based Frameworks
A B testing serves as the gold standard for large scale, data driven decision making in online businesses. To mitigate metric variability and enhance testing sensitivity, control variates and regression adjustment have emerged as prominent variance reduction techniques, leveraging pre experiment data to improve estimator performance. Over the past decade, these methods have spawned numerous derivatives, yet their theoretical connections and comparative properties remain underexplored. In this p…