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

• 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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