Journal of Machine Learning Research

The Journal of Machine Learning Research (JMLR), established in 2000, provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online.

News

• 2026.03.02: Volume 26 completed; Volume 27 began.
• 2025.02.10: Volume 25 completed; Volume 26 began.
• 2024.02.18: Volume 24 completed; Volume 25 began.
• 2023.01.20: Volume 23 completed; Volume 24 began.
• 2022.07.20: New special issue on climate change.
• 2022.02.18: New blog post: Retrospectives from 20 Years of JMLR .
• 2022.01.25: Volume 22 completed; Volume 23 began.
• 2021.12.02: Message from outgoing co-EiC Bernhard Schölkopf.
• 2021.02.10: Volume 21 completed; Volume 22 began.
• More news ...

Latest 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, 2026

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

  Simon Martin, Giulio Biroli, Francis Bach, 2026

• Error Analyses of Auto-Regressive Video Diffusion Models

  Jing Wang, Fengzhuo Zhang, Xiaoli Li, Vincent Y.~ F. Tan, Tianyu Pang, Chao Du, Aixin Sun, Zhuoran Yang, 2026

• Near-optimal Delta-convex Estimation of Lipschitz Functions

  Gábor Balázs, 2026

• The Sample Complexity of Parameter-Free Stochastic Convex Optimization

  Jared Lawrence, Ari Kalinsky, Hannah Bradfield, Yair Carmon, Oliver Hinder, 2026

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

  Yidong Zhou, Su I Iao, Hans-Georg Müller, 2026

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

  Wenlong Lyu, Yuheng Jia, Hui Liu, Junhui Hou, 2026

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

  Quentin Cohen-Solal, 2026

• Doubly Debiased Robust Subsampling for Transfer Learning

  Tao Wang, Weng Kee Wong, 2026

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

  Philip Sosnin, Matthew Wicker, Josh Collyer, Calvin Tsay, 2026

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

  Filippo Ascolani, Giacomo Zanella, 2026

• Underdamped Langevin MCMC with third order convergence

  Maximilian Scott, Dáire O'Kane, Andraž Jelinčič, James Foster, 2026

• Approximation-Free Differentiable Oblique Decision Trees

  Subrat Prasad Panda, Blaise Genest, Arvind Easwaran, 2026

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

  Ruiqi Zhang, Jingfeng Wu, Licong Lin, Peter L. Bartlett, 2026

• Adaptive Nonparametric Perturbations of Parametric Models with Generalized Bayes

  Bohan Wu, Eli N. Weinstein, Sohrab Salehi, Yixin Wang, David M. Blei, 2026

• 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, 2026

• Gradient Span Algorithms Make Predictable Progress in High Dimension

  Felix Benning, Leif Döring, 2026

• 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, 2026

• Statistical Test for Attention in Transformers for Images and Time Series

  Tomohiro Shiraishi, Daiki Miwa, Teruyuki Katsuoka, Vo Nguyen Le Duy, Shuichi Nishino, Kouichi Taji, Ichiro Takeuchi, 2026

• Accelerating Constrained Sampling: A Large Deviations Approach

  Yingli Wang, Changwei Tu, Xiaoyu Wang, Lingjiong Zhu, 2026

• Learning general conditional independence structures via the neighbourhood lattice

  Arash A. Amini, Bryon Aragam, Qing Zhou, 2026

• Statistical guarantees for denoising reflected diffusion models

  Asbjørn Holk, Claudia Strauch, Lukas Trottner, 2026

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

  Tim Gyger, Reinhard Furrer, Fabio Sigrist, 2026

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

  Zekun Shi, Zheyuan Hu, Min Lin, Kenji Kawaguchi, 2026

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

  Nawaf Bou-Rabee, Bob Carpenter, Tore Selland Kleppe, Sifan Liu, 2026

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

  Yuze Han, Xiang Li, Zhihua Zhang, 2026

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

  Jae Ho Chang, Subhadeep Paul, 2026

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

  Rabanus Derr, Jessie Finocchiaro, Robert C. Williamson, 2026

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

  Siyuan Zhang, Nachuan Xiao, Xin Liu, 2026

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

  Axel F. Wolter, Tobias Sutter, 2026

• FLAGG: Flexible Autoregressive Graph Generation

  Samuel Cognolato, Alessandro Sperduti, Luciano Serafini, 2026

• Nested Subspace Learning with Flags

  Tom Szwagier, Xavier Pennec, 2026

• A Unified Approach to Analysis and Design of Denoising Markov Models

  Yinuo Ren, Grant M. Rotskoff, Lexing Ying, 2026

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

  Nikolaos Tsilivis, Eitan Gronich, Julia Kempe, Gal Vardi, 2026

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

  Yongcun Song, Zimeng Wang, Xiaoming Yuan, Hangrui Yue, 2026

• Statistical Learning Theory for Neural Operators

  Niklas Reinhardt, Sven Wang, Jakob Zech, 2026

• Deconvolution in unlinked linear models

  Fadoua Balabdaoui, Antonio Di Noia, Cécile Durot, 2026

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

  Yuka Hashimoto, Ayoub Hafid, Masahiro Ikeda, Hachem Kadri, 2026

• High-dimensional Parameter Transfer With Fused-Regularizer

  Zelin He, Ying Sun, Jingyuan Liu, Runze Li, 2026

• Exogenous Randomness Empowering Random Forests

  Tianxing Mei, Yingying Fan, Jinchi Lv, 2026

• Kernel Mean Embedding Deviation Subspace for Unsupervised Learning with Heterogeneous Data

  Luoyao Yu, Lixing Zhu, Ruoqing Zhu, Xuehu Zhu, 2026

• Deep Nonparametric Conditional Independence Tests for Images

  Marco Simnacher, Xiangnan Xu, Hani Park, Christoph Lippert, Sonja Greven, 2026

• Semi-supervised learning for linear extremile regression

  Rong Jiang, Jiangfeng Wang, Keming Yu, 2026

• Transfer Learning via Regularized Random-effects Linear Discriminant Analysis

  Hongzhe Zhang, Arnab Auddy, Hongzhe Li, 2026

• Cheap Bootstrap for Fast Uncertainty Quantification of Stochastic Gradient Descent

  Henry Lam, Zitong Wang, 2026

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

  Shu Liu, Stanley Osher, Wuchen Li, 2026

• Generalized Resubstitution for Regression Error Estimation

  Diego Marcondes, Ulisses Braga-Neto, 2026

• Transfer Conformal Predictive Inference for Regression

  Ce Zhang, Ting Li, Jinhan Xie, Linglong Kong, Bei Jiang, 2026

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

  Hongying Liu, Hao Wang, Haoran Chu, Yibo Wu, 2026

• Node Regression on Latent Position Random Graphs via Local Averaging

  Martin Gjorgjevski, Nicolas Keriven, Simon Barthelme, Yohann De Castro, 2026

• On the Relevance of Byzantine Robust Optimization Against Data Poisoning

  Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, 2026

• Best Arm Identification with Minimal Regret

  Junwen Yang, Vincent Y. F. Tan, Tianyuan Jin, 2026

• Convergence of Noise-Free Sampling Algorithms with Regularized Wasserstein Proximals

  Fuqun Han, Stanley Osher, Wuchen Li, 2026

• Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features

  Jiuqi Wang, Shangtong Zhang, 2026

• Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models

  Jiaqi Li, Johannes Schmidt-Hieber, Wei Biao Wu, 2026

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

  Wanli Hong, Shuyang Ling, 2026

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

  Zhanrui Cai, Sai Li, Xintao Xia, Linjun Zhang, 2026

• Demographic Parity in Regression and Classification Within the Unawareness Framework

  Vincent Divol, Solenne Gaucher, 2026

• Enhancing Accuracy in Generative Models via Knowledge Transfer

  Xinyu Tian, Xiaotong Shen, 2026

• Multi-relational Network Autoregression Model with Latent Group Structures

  Yimeng Ren, Xuening Zhu, Ganggang Xu, Yanyuan Ma, 2026

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

  Yuanhong Jiang, Dongmian Zou, Xiaoqun Zhang, Yu Guang Wang, 2026

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

  Jun-Lin Wang, Zi Xu, Hui-Ling Zhang, 2026

• Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent

  Adrien Schertzer, Loucas Pillaud-Vivien, 2026

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

  Quoc Thong Le Gia, Ian Hugh Sloan, Holger Wendland, 2026

• Differentially Private Best-Arm Identification

  Achraf Azize, Marc Jourdan, Aymen Al Marjani, Debabrota Basu, 2026

• Corruptions of Supervised Learning Problems: Typology and Mitigations

  Laura Iacovissi, Nan Lu, Robert C. Williamson, 2026

• 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, Alexandre Lacoste, Simon Lacoste-Julien, 2026

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

  Yuchen Zhu, Yufeng Zhang, Zhaoran Wang, Zhuoran Yang, Xiaohong Chen, 2026

• Minimax density estimation in the adversarial framework under local differential privacy

  Mélisande Albert, Juliette Chevallier, Béatrice Laurent, Ousmane Sacko, 2026

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

  Ali D. Kara, Serdar Yüksel, 2026

• Optimal Approximation and Generalization Errors for Deep Convolutional Neural Networks

  Jinxin Wang, Shao-Bo Lin, 2026

• Investigating the Histogram Loss in Regression

  Ehsan Imani, Kai Luedemann, Sam Scholnick-Hughes, Esraa Elelimy, Martha White, 2026

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

  Xianli Zeng, Kevin Jiang, Guang Cheng, Edgar Dobriban, 2026

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

  Yueyao Yu, Yin Zhang, 2026

• Kernel-based Distributed Learning

  Heng Lian, Xu Guo, 2026

• A Convex Framework for Confounding Robust Inference

  Kei Ishikawa, Niao He, Takafumi Kanamori, 2026

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

  Álvaro Gajardo, Hans-Georg Müller, 2026

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

  David Huk, Rilwan A. Adewoyin, Ritabrata Dutta, 2026

• Sparse Topic Modeling via Spectral Decomposition and Thresholding

  Huy Tran, Yating Liu, Claire Donnat, 2026

• Nonparametric generative modeling for time series via Schrödinger bridge

  Mohamed Hamdouche, Pierre Henry-Labordère, Huyên Pham, 2026

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

  Yexiong Lin, Yu Yao, Yuxuan Du, Jun Yu, Bo Han, Mingming Gong, Tongliang Liu, 2026

• Causal Influences over Social Learning Networks

  Mert Kayaalp, Ali H. Sayed, 2026

• Neural Exploitation and Exploration of Contextual Bandits

  Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He, 2026

• Knowledge Cascade: Reverse Knowledge Distillation on Nonparametric Multivariate Functional Estimation

  Luyang Fang, Haoran Lu, Yongkai Chen, Wenxuan Zhong, Ping Ma, 2026

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

  Muxuan Liang, Yang Ning, Maureen A Smith, Ying-Qi Zhao, 2026

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

  Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna, 2026

• The Role of Contextual Information in Best Arm Identification

  Masahiro Kato, Kaito Ariu, 2026

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

  Yuling Jiao, Yanming Lai, Yang Wang, Bokai Yan, 2026

• Online Bernstein-von Mises theorem

  Jeyong Lee, Junhyeok Choi, Minwoo Chae, 2026

• Covariate-dependent Hierarchical Dirichlet Processes

  Huizi Zhang, Sara Wade, Natalia Bochkina, 2026

• DCatalyst: A Unified Accelerated Framework for Decentralized Optimization

  TIanyu Cao, Xiaokai Chen, Gesualdo Scutari, 2026

• Boosted Control Functions: Distribution Generalization and Invariance in Confounded Models

  Nicola Gnecco, Jonas Peters, Sebastian Engelke, Niklas Pfister, 2026

• 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, 2026

• A Symplectic Analysis of Alternating Mirror Descent

  Jonas E. Katona, Xiuyuan Wang, Andre Wibisono, 2026

• Two-way Node Popularity Model for Directed and Bipartite Networks

  Bing-Yi Jing, Ting Li, Jiangzhou Wang, Ya Wang, 2026

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

  Yuchen Li, Laura Balzano, Deanna Needell, Hanbaek Lyu, 2026

• Bayesian Inference of Contextual Bandit Policies via Empirical Likelihood

  Jiangrong Ouyang, Mingming Gong, Howard Bondell, 2026

• A causal fused lasso for interpretable heterogeneous treatment effects estimation

  Oscar Hernan Madrid Padilla, Yanzhen Chen, Carlos Misael Madrid Padilla, Gabriel Ruiz, 2026

• Unsupervised Feature Selection via Nonnegative Orthogonal Constrained Regularized Minimization

  Yan Li, Defeng Sun, Liping Zhang, 2026

• 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, 2026

• Hierarchical Causal Models

  Eli N. Weinstein, David M. Blei, 2026

• Optimizing Attention with Mirror Descent: Generalized Max-Margin Token Selection

  Addison Kristanto Julistiono, Davoud Ataee Tarzanagh, Navid Azizan, 2026

• Adaptive Forward Stepwise: A Method for High Sparsity Regression

  Ivy Zhang, Robert Tibshirani, 2026

• Optimization and Generalization of Gradient Descent for Shallow ReLU Networks with Minimal Width

  Yunwen Lei, Puyu Wang, Yiming Ying, Ding-Xuan Zhou, 2026

• Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection

  Steven Adams, Andrea Patanè, Morteza Lahijanian, Luca Laurenti, 2026

• CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration

  Sophie Jaffard, Samuel Vaiter, Patricia Reynaud-Bouret, 2026

• Persistence Diagrams Estimation of Multivariate Piecewise Hölder-continuous Signals

  Hugo Henneuse, 2026

• Exploring Novel Uncertainty Quantification through Forward Intensity Function Modeling

  Yudong Wang, Zhi-Sheng Ye, Cheng Yong Tang, 2026

• Generative Bayesian Inference with GANs

  Yuexi Wang, Veronika Rockova, 2026

• Communication-efficient Distributed Statistical Inference for Massive Data with Heterogeneous Auxiliary Information

  Miaomiao Yu, Zhongfeng Jiang, Jiaxuan Li, Yong Zhou, 2026

• Decorrelated Local Linear Estimator: Inference for Non-linear Effects in High-dimensional Additive Models

  Zijian Guo, Wei Yuan, Cunhui Zhang, 2026

• Refined Risk Bounds for Unbounded Losses via Transductive Priors

  Jian Qian, Alexander Rakhlin, Nikita Zhivotovskiy, 2026

• A Common Interface for Automatic Differentiation

  Guillaume Dalle, Adrian Hill, 2026

• LazyDINO: Fast, Scalable, and Efficiently Amortized Bayesian Inversion via Structure-Exploiting and Surrogate-Driven Measure Transport

  Lianghao Cao, Joshua Chen, Michael Brennan, Thomas O'Leary-Roseberry, Youssef Marzouk, Omar Ghattas, 2026

• The Distribution of Ridgeless Least Squares Interpolators

  Qiyang Han, Xiaocong Xu, 2026

• Nonparametric Estimation of a Factorizable Density using Diffusion Models

  Hyeok Kyu Kwon, Dongha Kim, Ilsang Ohn, Minwoo Chae, 2026

• Learning Bayesian Network Classifiers to Minimize Class Variable Parameters

  Shouta Sugahara, Koya Kato, James Cussens, Maomi Ueno, 2026

• Simulation-based Calibration of Uncertainty Intervals under Approximate Bayesian Estimation

  Terrance D. Savitsky, Julie Gershunskaya, 2026

• An Anytime Algorithm for Good Arm Identification

  Marc Jourdan, Andrée Delahaye-Duriez, Clémence Réda, 2026

• Extrapolated Markov Chain Oversampling Method for Imbalanced Text Classification

  Aleksi Avela, Pauliina Ilmonen, 2026

• Neural Network Parameter-optimization of Gaussian Pre-marginalized Directed Acyclic Graphs

  Mehrzad Saremi, 2026

• Flexible Functional Treatment Effect Estimation

  Jiayi Wang, Raymond K. W. Wong, Xiaoke Zhang, Kwun Chuen Gary Chan, 2026

• Error Analysis for Deep ReLU Feedforward Density-Ratio Estimation with Bregman Divergence

  Siming Zheng, Guohao Shen, Yuanyuan Lin, Jian Huang, 2026

• A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design

  Rui Ai, Boxiang Lyu, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan, 2026

• UQLM: A Python Package for Uncertainty Quantification in Large Language Models

  Dylan Bouchard, Mohit Singh Chauhan, David Skarbrevik, Ho-Kyeong Ra, Viren Bajaj, Zeya Ahmad, 2026

• Nonlinear function-on-function regression by RKHS

  Peijun Sang, Bing Li, 2026

• Nonlocal Techniques for the Analysis of Deep ReLU Neural Network Approximations

  Cornelia Schneider, Mario Ullrich, Jan Vybíral, 2026

• A Data-Augmented Contrastive Learning Approach to Nonparametric Density Estimation

  Chenghao Li, Yuanyuan Lin, 2026

• Guaranteed Nonconvex Low-Rank Tensor Estimation via Scaled Gradient Descent

  Tong Wu, 2026

• skwdro: a library for Wasserstein distributionally robust machine learning

  Vincent Florian, Waïss Azizian, Franck Iutzeler, Jérôme Malick, 2026

• Extending Mean-Field Variational Inference via Entropic Regularization: Theory and Computation

  Bohan Wu, David M. Blei, 2026

• Stochastic Gradient Methods: Bias, Stability and Generalization

  Shuang Zeng, Yunwen Lei, 2026

• Classification Under Local Differential Privacy with Model Reversal and Model Averaging

  Caihong Qin, Yang Bai, 2026

• Identifying Weight-Variant Latent Causal Models

  Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi, 2026

• Efficient frequent directions algorithms for approximate decomposition of matrices and higher-order tensors

  Maolin Che, Yimin Wei, Hong Yan, 2026

• Online Detection of Changes in Moment--Based Projections: When to Retrain Deep Learners or Update Portfolios?

  Ansgar Steland, 2026

• The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks

  Sebastian Bieringer, Gregor Kasieczka, Maximilian F. Steffen, Mathias Trabs, 2026

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