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

• 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

• Estimation of Local Geometric Structure on Manifolds from Noisy Data

  Yariv Aizenbud, Barak Sober, 2025

• Ontolearn---A Framework for Large-scale OWL Class Expression Learning in Python

  Caglar Demir, Alkid Baci, N'Dah Jean Kouagou, Leonie Nora Sieger, Stefan Heindorf, Simon Bin, Lukas Blübaum, Alexander Bigerl, Axel-Cyrille Ngonga Ngomo, 2025

• Continuously evolving rewards in an open-ended environment

  Richard M. Bailey, 2025

• Recursive Causal Discovery

  Ehsan Mokhtarian, Sepehr Elahi, Sina Akbari, Negar Kiyavash, 2025

• Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings

  Henrik von Kleist, Alireza Zamanian, Ilya Shpitser, Narges Ahmidi, 2025

• On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations

  Antoine Godichon-Baggioni, Nicklas Werge, 2025

• Determine the Number of States in Hidden Markov Models via Marginal Likelihood

  Yang Chen, Cheng-Der Fuh, Chu-Lan Michael Kao, 2025

• Variance-Aware Estimation of Kernel Mean Embedding

  Geoffrey Wolfer, Pierre Alquier, 2025

• Scaling ResNets in the Large-depth Regime

  Pierre Marion, Adeline Fermanian, Gérard Biau, Jean-Philippe Vert, 2025

• A Comparative Evaluation of Quantification Methods

  Tobias Schumacher, Markus Strohmaier, Florian Lemmerich, 2025

• Lightning UQ Box: Uncertainty Quantification for Neural Networks

  Nils Lehmann, Nina Maria Gottschling, Jakob Gawlikowski, Adam J. Stewart, Stefan Depeweg, Eric Nalisnick, 2025

• Scaling Data-Constrained Language Models

  Niklas Muennighoff, Alexander M. Rush, Boaz Barak, Teven Le Scao, Aleksandra Piktus, Nouamane Tazi, Sampo Pyysalo, Thomas Wolf, Colin Raffel, 2025

• Curvature-based Clustering on Graphs

  Yu Tian, Zachary Lubberts, Melanie Weber, 2025

• Composite Goodness-of-fit Tests with Kernels

  Oscar Key, Arthur Gretton, François-Xavier Briol, Tamara Fernandez, 2025

• PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark

  Jianqing Zhang, Yang Liu, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Jian Cao, 2025

• The Effect of SGD Batch Size on Autoencoder Learning: Sparsity, Sharpness, and Feature Learning

  Nikhil Ghosh, Spencer Frei, Wooseok Ha, Bin Yu, 2025

• Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data

  Pan Zhao, Julie Josse, Shu Yang, 2025

• DAGs as Minimal I-maps for the Induced Models of Causal Bayesian Networks under Conditioning

  Xiangdong Xie, Jiahua Guo, Yi Sun, 2025

• Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization

  Shouri Hu, Haowei Wang, Zhongxiang Dai, Bryan Kian Hsiang Low, Szu Hui Ng, 2025

• Manifold Fitting under Unbounded Noise

  Zhigang Yao, Yuqing Xia, 2025

• Learning Global Nash Equilibrium in Team Competitive Games with Generalized Fictitious Cross-Play

  Zelai Xu, Chao Yu, Yancheng Liang, Yi Wu, Yu Wang, 2025

• Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models

  Xuefeng Gao, Hoang M. Nguyen, Lingjiong Zhu, 2025

• Extremal graphical modeling with latent variables via convex optimization

  Sebastian Engelke, Armeen Taeb, 2025

• On the Approximation of Kernel functions

  Paul Dommel, Alois Pichler, 2025

• Efficient and Robust Semi-supervised Estimation of Average Treatment Effect with Partially Annotated Treatment and Response

  Jue Hou, Rajarshi Mukherjee, Tianxi Cai, 2025

• Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning

  Kuangyu Ding, Jingyang Li, Kim-Chuan Toh, 2025

• Optimizing Data Collection for Machine Learning

  Rafid Mahmood, James Lucas, Jose M. Alvarez, Sanja Fidler, Marc T. Law, 2025

• Unbalanced Kantorovich-Rubinstein distance, plan, and barycenter on nite spaces: A statistical perspective

  Shayan Hundrieser, Florian Heinemann, Marcel Klatt, Marina Struleva, Axel Munk, 2025

• Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding

  Jiajing Zheng, Alexander D'Amour, Alexander Franks, 2025

• Rank-one Convexification for Sparse Regression

  Alper Atamturk, Andres Gomez, 2025

• gsplat: An Open-Source Library for Gaussian Splatting

  Vickie Ye, Ruilong Li, Justin Kerr, Matias Turkulainen, Brent Yi, Zhuoyang Pan, Otto Seiskari, Jianbo Ye, Jeffrey Hu, Matthew Tancik, Angjoo Kanazawa, 2025

• Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming

  Sen Na, Michael Mahoney, 2025

• Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds

  Clément Bonet, Lucas Drumetz, Nicolas Courty, 2025

• Accelerating optimization over the space of probability measures

  Shi Chen, Qin Li, Oliver Tse, Stephen J. Wright, 2025

• Bayesian Multi-Group Gaussian Process Models for Heterogeneous Group-Structured Data

  Didong Li, Andrew Jones, Sudipto Banerjee, Barbara E. Engelhardt, 2025

• Orthogonal Bases for Equivariant Graph Learning with Provable k-WL Expressive Power

  Jia He, Maggie Cheng, 2025

• Optimal Experiment Design for Causal Effect Identification

  Sina Akbari, Jalal Etesami, Negar Kiyavash, 2025

• Mean Aggregator is More Robust than Robust Aggregators under Label Poisoning Attacks on Distributed Heterogeneous Data

  Jie Peng, Weiyu Li, Stefan Vlaski, Qing Ling, 2025

• The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond

  Jiin Woo, Gauri Joshi, Yuejie Chi, 2025

• depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers

  Kaichao You, Runsheng Bai, Meng Cao, Jianmin Wang, Ion Stoica, Mingsheng Long, 2025

• The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise

  Shuze Daniel Liu, Shuhang Chen, Shangtong Zhang, 2025

• Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick

  Xiyuan Wang, Pan Li, Muhan Zhang, 2025

• Directed Cyclic Graphs for Simultaneous Discovery of Time-Lagged and Instantaneous Causality from Longitudinal Data Using Instrumental Variables

  Wei Jin, Yang Ni, Amanda B. Spence, Leah H. Rubin, Yanxun Xu, 2025

• Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions

  Dapeng Yao, Fangzheng Xie, Yanxun Xu, 2025

• Regularizing Hard Examples Improves Adversarial Robustness

  Hyungyu Lee, Saehyung Lee, Ho Bae, Sungroh Yoon, 2025

• Random ReLU Neural Networks as Non-Gaussian Processes

  Rahul Parhi, Pakshal Bohra, Ayoub El Biari, Mehrsa Pourya, Michael Unser, 2025

• Riemannian Bilevel Optimization

  Jiaxiang Li, Shiqian Ma, 2025

• Supervised Learning with Evolving Tasks and Performance Guarantees

  Verónica Álvarez, Santiago Mazuelas, Jose A. Lozano, 2025

• Error estimation and adaptive tuning for unregularized robust M-estimator

  Pierre C. Bellec, Takuya Koriyama, 2025

• From Sparse to Dense Functional Data in High Dimensions: Revisiting Phase Transitions from a Non-Asymptotic Perspective

  Shaojun Guo, Dong Li, Xinghao Qiao, Yizhu Wang, 2025

• Locally Private Causal Inference for Randomized Experiments

  Yuki Ohnishi, Jordan Awan, 2025

• Estimating Network-Mediated Causal Effects via Principal Components Network Regression

  Alex Hayes, Mark M. Fredrickson, Keith Levin, 2025

• Selective Inference with Distributed Data

  Sifan Liu, Snigdha Panigrahi, 2025

• Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization

  Tianyi Lin, Chi Jin, Michael I. Jordan, 2025

• An Axiomatic Definition of Hierarchical Clustering

  Ery Arias-Castro, Elizabeth Coda, 2025

• Test-Time Training on Video Streams

  Renhao Wang, Yu Sun, Arnuv Tandon, Yossi Gandelsman, Xinlei Chen, Alexei A. Efros, Xiaolong Wang, 2025

• Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback

  Boxin Zhao, Lingxiao Wang, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Chaochao Chen, Mladen Kolar, 2025

• A Random Matrix Approach to Low-Multilinear-Rank Tensor Approximation

  Hugo Lebeau, Florent Chatelain, Romain Couillet, 2025

• Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agents

  Marco Pleines, Matthias Pallasch, Frank Zimmer, Mike Preuss, 2025

• Enhancing Graph Representation Learning with Localized Topological Features

  Zuoyu Yan, Qi Zhao, Ze Ye, Tengfei Ma, Liangcai Gao, Zhi Tang, Yusu Wang, Chao Chen, 2025

• Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization

  Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis, 2025

• DisC2o-HD: Distributed causal inference with covariates shift for analyzing real-world high-dimensional data

  Jiayi Tong, Jie Hu, George Hripcsak, Yang Ning, Yong Chen, 2025

• Bayes Meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes

  Charles Riou, Pierre Alquier, Badr-Eddine Chérief-Abdellatif, 2025

• Efficiently Escaping Saddle Points in Bilevel Optimization

  Minhui Huang, Xuxing Chen, Kaiyi Ji, Shiqian Ma, Lifeng Lai, 2025

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