Nonparametric Regression

Follow this topic Ask about this topic

Momentum

7 papers in the last four weeks, with none the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 23

All topics

RoBART: Bayesian Additive Regression Trees with Tree-Specific Rotations
Oct 7, 2026Jeongung Heo, Seonghyun JeongNonparametric Regression
Yonsei University
Minimax Additive Regression under Unknown Dependent Designs
Sep 30, 2026Baptiste Ferrere, Fabrice Gamboa, Jean-Michel LoubesNonparametric Regression Minimax Estimation
Université de Toulouse EDF R&D · Université de Toulouse ANITI
Optimal Tradeoffs Between Network Size and Parameter Magnitude in Neural Approximation and Minimax Regression
Sep 22, 2026Baicheng Li, Zuowei Shen, Haizhao Yang +1Neural Network Optimization Nonparametric Regression
Department of Mathematics, University of Maryland, College Park, MD 20742-4015, USA. · Department of Mathematics, National University of Singapore, Singapore 119076. · Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China.
Generalized Deep Regression for Repeated Measurements
Sep 22, 2026Kexuan LiNeural Network Generalization ReLU Neural Networks
Bristol Myers Squibb
Riemannian Simultaneous Inference for Tangent Vector Field Regression
Sep 18, 2026Xiaotian Chang, Yangdi Jiang, Qirui HuNonparametric Regression
Division of Mathematical Sciences, Nanyang Technological University. · School of Statistics and Data Science, Shanghai University of Finance and Economics.
MiNCE: Nonparametric, Strongly Consistent Confidence Envelopes for Band-Limited Functions and their Smoothed Spectra
Sep 8, 2026Balázs Csanád Csáji, Bálint HorváthConfidence Region Estimation Nonparametric Regression
HUN-REN SZTAKI: Institute for Computer Science and Control, Budapest, Hungary · Institute of Mathematics, Eötvös Loránd University (ELTE), Budapest, Hungary
Optimal estimation for Functional Linear Regression with Noisy Discretized Data
Sep 8, 2026Sixtine SphabmixayNonparametric Regression Minimax Estimation
Université Paris Cité, CNRS, MAP5, F-75006 Paris, France
Distributed Lag Neural Additive Models
Sep 7, 2026Calle Helmersson, Shivang Pandey, Leonardo Olivetti +1Nonparametric Regression Neural Network Interpretability
Department of Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden · Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden · Swedish Centre for Impacts of Climate Extremes (climes), Uppsala University, Uppsala, Sweden +4
Conditional Diffusion for Nonparametric Instrumental Variable Quantile Regression
Aug 8, 2026Xingdong Feng, Xinhong Jiang, Yuling Jiao +2Quantile Regression Nonparametric Regression
School of Statistics and Data Science, Institute of Data Science and Statistics, Shanghai University of Finance and Economics, Shanghai 200433, China · School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China · School of Artificial Intelligence, and Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan, 430072, China +1
Verifiable Regularity Criterion for Conditional Expectation Operators and Conditional Mean Embeddings with Applications to Nonparametric Regression, Bayesian Inverse Problems, and Koopman Operators
Aug 6, 2026Maximiliano Hertel, Ilja Klebanov, Manuel Schaller +1Nonparametric Regression
Optimization-based Control Group, Institute of Mathematics, Technische Universität Ilmenau, Germany · Department of Mathematics and Computer Science, Freie Universität Berlin, Germany · Faculty of Mathematics, Chemnitz University of Technology, Germany
The Noise Premium in Adversarial Training for Kernel Regression
Jul 30, 2026Yiling Xie, Xiaoming HuoAdversarial Training Kernel Regression
Department of Decision Analytics and Operations, City University of Hong Kong · H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology
Automatic knot selection in smooth additive models
Jul 23, 2026Nicolás Carrizosa, Vanesa Guerrero, María DurbánNonparametric Regression
Department of Statistics, Operations Research and Mathematics Education, Universidad de Oviedo · Department of Statistics, Universidad Carlos III de Madrid
Adaptive deep nonparametric regression from dependent data under covariate shift
Jul 22, 2026William Kengne, Ehud Mossa OckegnaQuantile Regression Covariate Shift
Université Jean Monnet, ICJ UMR5208, CNRS, Ecole Centrale de Lyon, INSA Lyon, Universite Claude Bernard Lyon 1, 42000 Saint-Étienne, France
Black-Box Assisted Regression: Phase Transitions and Minimax Optimality
Jun 24, 2026Yan ZhouResidual Learning Nonparametric Regression
School of Mathematics and Statistics, Changsha University of Science and Technology.
Generalized nonparametric regression in reproducing kernel Hilbert spaces: Consistency and rates of convergence
Jun 22, 2026Ioannis KalogridisNonparametric Regression Reproducing Kernel Hilbert Spaces
School of Mathematics and Statistics, University of Glasgow, United Kingdom
Posterior Contraction of Lévy Adaptive B-spline Regression in Besov Spaces
May 19, 2026Jeunghun Oh, Sewon Park, Jaeyong LeeBayesian Inference Nonparametric Regression
Department of Statistics, Seoul National University. · Department of Statistics, Sookmyung Women’s University.
Shallow ReLU $^s$ Networks in $L^p$ -Type and Sobolev Spaces: Approximation and Path-Norm Controlled Generalization
May 18, 2026Weizhao Li, Fanghui Liu, Lei ShiShallow Neural Networks Nonparametric Regression
School of Mathematical Sciences Fudan University Shanghai, China · School of Mathematical Sciences Institute of Natural Sciences and MOE-LSC2026 Shanghai Jiao Tong University Shanghai, China · School of Mathematical Sciences Shanghai Key Laboratory for Contemporary Applied Mathematics25 Fudan University Shanghai, China
On Uniform Error Bounds for Kernel Regression under Non-Gaussian Noise
May 10, 2026Johannes Teutsch, Oleksii Molodchyk, Marion Leibold +2Kernel Regression Uncertainty Quantification
Chair of Automatic Control Engineering, Department of Computer Engineering, Technical University of Munich, 80333 Munich, Germany · Institute of Control Systems, Hamburg University of Technology, 21073 Hamburg, Germany · Department of Electrical and Computer Engineering, National University of Singapore, 117583 Singapore
ParaRNN: An Interpretable and Parallelizable Recurrent Neural Network for Time-Dependent Data
May 4, 2026Yuxi Cai, Lan Li, Feiqing Huang +1Interpretable ML Recurrent Neural Networks
Department of Statistics and Actuarial Science, University of Hong Kong
Adversarial Robustness of NTK Neural Networks
Apr 28, 2026Yuxuan HouNeural Network Robustness Adversarial Robustness
1Qiuzhen College, Tsinghua University
Identifiable Convex-Concave Regression via Sub-gradient Regularised Least Squares
Jun 22, 2025William ChungParameter Identifiability Nonparametric Regression
Department of Decision Analytics and Operations, City University of Hong Kong, Hong Kong
Deep learning with missing data
Apr 21, 2025Tianyi Ma, Tengyao Wang, Richard J. SamworthNeural Network Generalization Learning with Missing Data
Statistical Laboratory, University of Cambridge · Department of Statistics, London School of Economics
Statistical Properties of Deep Neural Networks with Dependent Data
Oct 14, 2024Chad BrownNonparametric Regression Neural Network Approximation Theory
Department of Economics, University of Colorado Boulder, 80309, USA.

Oct 7, 2026, stat.MLJ/K move · Enter open · S save

RoBART: Bayesian Additive Regression Trees with Tree-Specific Rotations

Jeongung Heo, Seonghyun Jeong

Yonsei University

Nonparametric Regression

Open paper AskPDF arXiv

Bayesian additive regression trees (BART) can require many splits to approximate boundaries misaligned with the predictor axes. RoBART assigns each tree a rotation shared by all internal nodes, retaining axis-aligned splits in rotated coordinates and constant leaves. We jointly propose a Givens rotation sequence and cutpoints on the resulting grid by Metropolis-Hastings and establish reversibility with respect to the conditional posterior with leaf means integrated out. For additive functions with component-specific rotations and anisotropic Hölder smoothness, we prove posterior contraction in empirical

L_2

distance and for the noise standard deviation. Under the stated prior, design, and grid conditions, with fixed numbers of predictors, trees, and components and no more components than trees, the rate is a sum of componentwise rates determined by smoothness and the number of rotated coordinates used. We also establish a posterior contraction lower bound showing that there exist functions for which RoBART adapts to the intrinsic dimension but axis-aligned BART does not.

Momentum

Related topics

Latest papers 23