JMLR 2026

137 papers

Bridging Domain Invariance and Diversity: A Fine-Grained Risk Bound for Domain Generalization

Xi Wang, Liang Bai, Xian Yang, Richard Yi Da Xu, Jiye Liang

High-Dimensional Analysis of Gradient Flow for Extensive-Width Quadratic Neural Networks

Simon Martin, Giulio Biroli, Francis Bach

Error Analyses of Auto-Regressive Video Diffusion Models

Jing Wang, Fengzhuo Zhang, Xiaoli Li, Vincent Y.~ F. Tan, Tianyu Pang

Near-optimal Delta-convex Estimation of Lipschitz Functions

Gabor Balazs

The Sample Complexity of Parameter-Free Stochastic Convex Optimization

Jared Lawrence, Ari Kalinsky, Hannah Bradfield, Yair Carmon, Oliver Hinder

End-to-End Deep Learning for Predicting Metric Space-Valued Outputs

Yidong Zhou, Su I Iao, Hans-Georg Müller

Graph-based Clustering Revisited: A Relaxation of Kernel k-Means Perspective

Wenlong Lyu, Yuheng Jia, Hui LIU, Junhui Hou

Learning to Play Two-Player Perfect-Information Games without Knowledge

Quentin Cohen-Solal

Doubly Debiased Robust Subsampling for Transfer Learning

Tao Wang, Weng Kee Wong

Abstract Gradient Training: A Unified Certification Framework for Data Poisoning, Unlearning, and Differential Privacy

Philip Sosnin, Matthew Wicker, Josh Collyer, Calvin Tsay

Mixing times of data-augmentation Gibbs samplers for high-dimensional probit regression

Filippo Ascolani, Giacomo Zanella

Underdamped Langevin MCMC with third order convergence

Maximilian Scott, Dáire O'Kane, Andraž Jelinčič, James Foster

Approximation-Free Differentiable Oblique Decision Trees

Subrat Prasad Panda, Blaise Genest, Arvind Easwaran

Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes

Ruiqi Zhang, Jingfeng Wu, Licong Lin, Peter L. Bartlett

Adaptive Nonparametric Perturbations of Parametric Models with Generalized Bayes

Bohan Wu, Eli N. Weinstein, Sohrab Salehi, Yixin Wang, David M. Blei

Robust training of implicit generative models for multivariate and heavy-tailed distributions with an invariant statistical loss

José Manuel de Frutos, Manuel A. Vázquez, Pablo M. Olmos, Joaquín Míguez

Gradient Span Algorithms Make Predictable Progress in High Dimension

Felix Benning, Leif Döring

py/cuTAGI: An Open-Source Library for Tractable Approximate Gaussian Inference in Bayesian Neural Networks

Luong-Ha Nguyen, James-A. Goulet, Miquel Florensa-Montilla, Van-Dai Vuong

Statistical Test for Attention in Transformers for Images and Time Series

Tomohiro Shiraishi, Daiki Miwa, Teruyuki Katsuoka, Vo Nguyen Le Duy, Shuichi Nishino

Accelerating Constrained Sampling: A Large Deviations Approach

Yingli Wang, Changwei Tu, Xiaoyu Wang, Lingjiong Zhu

Learning general conditional independence structures via the neighbourhood lattice

Arash A. Amini, Bryon Aragam, Qing Zhou

Statistical guarantees for denoising reflected diffusion models

Asbjørn Holk, Claudia Strauch, Lukas Trottner

Vecchia-Inducing-Points Full-Scale Approximations for Gaussian Processes

Tim Gyger, Reinhard Furrer, Fabio Sigrist

STDE++: Polynomial-Time Amortization for Linear Differential Operators

Zekun Shi, Zheyuan Hu, Min Lin, Kenji Kawaguchi

The Within-Orbit Adaptive Leapfrog No-U-Turn Sampler

Nawaf Bou-Rabee, Bob Carpenter, Tore Selland Kleppe, Sifan Liu

Finite-Time Decoupled Convergence in Nonlinear Two-Time-Scale Stochastic Approximation

Yuze Han, Xiang Li, Zhihua Zhang

Embedding Network Autoregression for Time Series Analysis and Causal Peer Effect Inference

Jae Ho Chang, Subhadeep Paul

Three Types of Calibration using Properties and their Semantic and Formal Relationships

Rabanus Derr, Jessie Finocchiaro, Robert C. Williamson

Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex Optimization

Siyuan Zhang, Nachuan Xiao, Xin Liu

A Two-Timescale Primal-Dual Framework for Reinforcement Learning via Online Dual Variable Guidance

Axel F. Wolter, Tobias Sutter

FLAGG: Flexible Autoregressive Graph Generation

Samuel Cognolato, Alessandro Sperduti, Luciano Serafini

Nested Subspace Learning with Flags

Tom Szwagier, Xavier Pennec

A Unified Approach to Analysis and Design of Denoising Markov Models

Yinuo Ren, Grant M. Rotskoff, Lexing Ying

Flavors of Margin: Implicit Bias of Steepest Descent in Homogeneous Neural Networks

Nikolaos Tsilivis, Eitan Gronich, Julia Kempe, Gal Vardi

A Single-Loop Stochastic Proximal Quasi-Newton Method for Large-Scale Nonsmooth Convex Optimization

Yongcun Song, Zimeng Wang, Xiaoming Yuan, Hangrui Yue

Statistical Learning Theory for Neural Operators

Niklas Reinhardt, Sven Wang, Jakob Zech

Deconvolution in unlinked linear models

Fadoua Balabdaoui, Antonio Di Noia, Cécile Durot

Spectral Truncation Kernels: Noncommutativity in C*-algebraic Kernel Machines

Yuka Hashimoto, Ayoub Hafid, Masahiro Ikeda, Hachem Kadri

High-dimensional Parameter Transfer With Fused-Regularizer

Zelin He, Ying Sun, Jingyuan Liu, Runze Li

Exogenous Randomness Empowering Random Forests

Tianxing Mei, Yingying Fan, Jinchi Lv

Kernel Mean Embedding Deviation Subspace for Unsupervised Learning with Heterogeneous Data

Luoyao Yu, Lixing Zhu, Ruoqing Zhu, Xuehu ZHU

Deep Nonparametric Conditional Independence Tests for Images

Marco Simnacher, Xiangnan Xu, Hani Park, Christoph Lippert, Sonja Greven

Semi-supervised learning for linear extremile regression

Rong Jiang, Jiangfeng Wang, Keming Yu

Transfer Learning via Regularized Random-effects Linear Discriminant Analysis

Hongzhe Zhang, Arnab Auddy, Hongzhe Li

Cheap Bootstrap for Fast Uncertainty Quantification of Stochastic Gradient Descent

Henry Lam, Zitong Wang

A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equation

Shu Liu, Stanley Osher, Wuchen Li

Generalized Resubstitution for Regression Error Estimation

Diego Marcondes, Ulisses Braga-Neto

Transfer Conformal Predictive Inference for Regression

Ce Zhang, ting li, Jinhan Xie, Linglong Kong, Bei Jiang

Towards Convexity in Anomaly Detection: A New Formulation of SSLM with Unique Optimal Solutions

Hongying Liu, Hao Wang, Haoran Chu, Yibo Wu

Node Regression on Latent Position Random Graphs via Local Averaging

Martin Gjorgjevski, Nicolas Keriven, Simon Barthelmé, Yohann De Castro

On the Relevance of Byzantine Robust Optimization Against Data Poisoning

Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot

Best Arm Identification with Minimal Regret

Junwen Yang, Vincent Y. F. Tan, Tianyuan Jin

Convergence of Noise-Free Sampling Algorithms with Regularized Wasserstein Proximals

Fuqun Han, Stanley Osher, Wuchen Li

Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features

Jiuqi Wang, Shangtong Zhang

Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models

Jiaqi Li, Johannes Schmidt-Hieber, Wei Biao Wu

Beyond Unconstrained Features: Neural Collapse for Shallow Neural Networks with General Data

Wanli Hong, Shuyang Ling

Differentially Private Estimation and Inference in High-Dimensional Regression with FDR Control

Zhanrui Cai, Sai Li, Xintao Xia, Linjun Zhang

Demographic Parity in Regression and Classification Within the Unawareness Framework

Vincent Divol, Solenne Gaucher

Enhancing Accuracy in Generative Models via Knowledge Transfer

Xinyu Tian, Xiaotong Shen

Multi-relational Network Autoregression Model with Latent Group Structures

Yimeng Ren, Xuening Zhu, Ganggang Xu, Yanyuan Ma

Limiting Over-Smoothing and Over-Squashing of Graph Message Passing by Deep Scattering Transforms

Yuanhong Jiang, Dongmian Zou, Xiaoqun Zhang, Yu Guang Wang

A Fully Parameter-Free Second-Order Algorithm for Convex-Concave Minimax Problems

Jun-Lin Wang, Zi Xu, Hui-Ling Zhang

Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent

Adrien Schertzer, Loucas Pillaud-Vivien

Vector-Valued Gaussian Processes for Approximating Divergence- or Rotation-free Vector Fields

Quoc Thong Le Gia, Ian Hugh Sloan, Holger Wendland

Differentially Private Best-Arm Identification

Achraf Azize, Marc Jourdan, Aymen Al Marjani, Debabrota Basu

Corruptions of Supervised Learning Problems: Typology and Mitigations

Laura Iacovissi, Nan Lu, Robert C. Williamson

Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies

Sébastien Lachapelle, Pau Rodríguez López, Yash Sharma, Katie Everett, Rémi Le Priol

A Mean-Field Analysis of Neural Stochastic Gradient Descent-Ascent for Functional Minimax Optimization

Yuchen Zhu, Yufeng Zhang, Zhaoran Wang, Zhuoran Yang, Xiaohong Chen

Minimax density estimation in the adversarial framework under local differential privacy

Mélisande Albert, Juliette Chevallier, Béatrice Laurent, Ousmane Sacko

Approximations and Learning for Continuous State and Action MDPs under Average Cost Criteria

Ali D. Kara, Serdar Yüksel

Optimal Approximation and Generalization Errors for Deep Convolutional Neural Networks

Jinxin Wang, Shao-Bo Lin

Investigating the Histogram Loss in Regression

Ehsan Imani, Kai Luedemann, Sam Scholnick-Hughes, Esraa Elelimy, Martha White

Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing

Xianli Zeng, Kevin Jiang, Guang Cheng, Edgar Dobriban

Why "Classic" Transformers Are Shallow and A Depth-Enabling Technique

Yueyao Yu, Yin Zhang

Kernel-based Distributed Learning

Heng Lian, Xu Guo

A Convex Framework for Confounding Robust Inference

Kei Ishikawa, Niao He, Takafumi Kanamori

Global Fréchet Manifold Learning for Random Objects, With Application to Low-Dimensional Wasserstein Representations of Distributional Data

Álvaro Gajardo, Hans-Georg Müller

Probabilistic Rainfall Downscaling: Joint Generalized Neural Models with Censored Spatial Gaussian Copula

David Huk, Rilwan A. Adewoyin, Ritabrata Dutta

Sparse Topic Modeling via Spectral Decomposition and Thresholding

Huy Tran, Yating Liu, Claire Donnat

Nonparametric generative modeling for time series via Schrödinger bridge

Mohamed Hamdouche, Pierre Henry-Labordère, Huyen PHAM

Do We Need to Penalize Variance of Losses for Learning with Label Noise?

Yexiong Lin, Yu Yao, Yuxuan Du, Jun Yu, Bo Han

Causal Influences over Social Learning Networks

Mert Kayaalp, Ali H. Sayed

Neural Exploitation and Exploration of Contextual Bandits

Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He

Knowledge Cascade: Reverse Knowledge Distillation on Nonparametric Multivariate Functional Estimation

Luyang Fang, Haoran Lu, Yongkai Chen, Wenxuan Zhong, Ping Ma

Inference with non-differentiable surrogate loss in a general high-dimensional classification framework

Muxuan Liang, Yang Ning, Maureen A Smith, Ying-Qi Zhao

A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks

Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna

The Role of Contextual Information in Best Arm Identification

Masahiro Kato, Kaito Ariu

Transformers Can Overcome the Curse of Dimensionality: A Theoretical Study from an Approximation Perspective

Yuling Jiao, Yanming Lai, Yang Wang, Bokai Yan

Online Bernstein-von Mises theorem

Jeyong Lee, Junhyeok Choi, Minwoo Chae

Covariate-dependent Hierarchical Dirichlet Processes

Huizi Zhang, Sara Wade, Natalia Bochkina

DCatalyst: A Unified Accelerated Framework for Decentralized Optimization

Tianyu Cao, Xiaokai Chen, Gesualdo Scutari

Boosted Control Functions: Distribution Generalization and Invariance in Confounded Models

Nicola Gnecco, Jonas Peters, Sebastian Engelke, Niklas Pfister

Contrasting Local and Global Modeling with Machine Learning and Satellite Data: A Case Study Estimating Tree Canopy Height in African Savannas

Esther Rolf, Lucia Gordon, Milind Tambe, Andrew Davies

A Symplectic Analysis of Alternating Mirror Descent

Jonas E. Katona, Xiuyuan Wang, Andre Wibisono

Bing-Yi Jing, ting li, Jiangzhou Wang, Ya Wang

Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization

Yuchen Li, Laura Balzano, Deanna Needell, Hanbaek Lyu

Bayesian Inference of Contextual Bandit Policies via Empirical Likelihood

Jiangrong Ouyang, Mingming Gong, Howard Bondell

A causal fused lasso for interpretable heterogeneous treatment effects estimation

OSCAR HERNAN MADRID PADILLA, Yanzhen Chen, Carlos Misael Madrid Padilla, Gabriel Ruiz

Unsupervised Feature Selection via Nonnegative Orthogonal Constrained Regularized Minimization

Yan Li, Defeng Sun, Liping Zhang

Reparameterized Complex-valued Neurons Can Efficiently Learn More than Real-valued Neurons via Gradient Descent

Jin-Hui Wu, Shao-Qun Zhang, Yuan Jiang, Zhi-Hua Zhou