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.
JMLR has a commitment to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1533-7928) immediately upon receipt. Until the end of 2004, paper volumes (ISSN 1532-4435) were published 8 times annually and sold to libraries and individuals by the MIT Press. Paper volumes (ISSN 1532-4435) are now published and sold by Microtome Publishing.
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
- DRM Revisited: A Complete Error Analysis
- Yuling Jiao, Ruoxuan Li, Peiying Wu, Jerry Zhijian Yang, Pingwen Zhang, 2025.
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- Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF
- Han Shen, Zhuoran Yang, Tianyi Chen, 2025.
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- Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers
- Fan Yang, Hongyang R. Zhang, Sen Wu, Christopher Re, Weijie J. Su, 2025.
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- Score-based Causal Representation Learning: Linear and General Transformations
- Burak Var{{\i}}c{{\i}}, Emre Acartürk, Karthikeyan Shanmugam, Abhishek Kumar, Ali Tajer, 2025.
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- On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension
- Saptarshi Chakraborty, Peter L. Bartlett, 2025.
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- Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms
- Keru Wu, Yuansi Chen, Wooseok Ha, Bin Yu, 2025.
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- Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles
- Lesi Chen, Yaohua Ma, Jingzhao Zhang, 2025.
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- Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos
- Shao-Bo Lin, Xiaotong Liu, Di Wang, Hai Zhang, Ding-Xuan Zhou, 2025.
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- On Global and Local Convergence of Iterative Linear Quadratic Optimization Algorithms for Discrete Time Nonlinear Control
- Vincent Roulet, Siddhartha Srinivasa, Maryam Fazel, Zaid Harchaoui, 2025.
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- A Decentralized Proximal Gradient Tracking Algorithm for Composite Optimization on Riemannian Manifolds
- Lei Wang, Le Bao, Xin Liu, 2025.
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- Learning conditional distributions on continuous spaces
- Cyril Benezet, Ziteng Cheng, Sebastian Jaimungal, 2025.
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- A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs
- Lukas Zierahn, Dirk van der Hoeven, Tal Lancewicki, Aviv Rosenberg, Nicolò Cesa-Bianchi, 2025.
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- Error bounds for particle gradient descent, and extensions of the log-Sobolev and Talagrand inequalities
- Rocco Caprio, Juan Kuntz, Samuel Power, Adam M. Johansen, 2025.
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- Linear Hypothesis Testing in High-Dimensional Expected Shortfall Regression with Heavy-Tailed Errors
- Gaoyu Wu, Jelena Bradic, Kean Ming Tan, Wen-Xin Zhou, 2025.
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- Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling
- Antoine Chatalic, Nicolas Schreuder, Ernesto De Vito, Lorenzo Rosasco, 2025.
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- Distribution Free Tests for Model Selection Based on Maximum Mean Discrepancy with Estimated Parameters
- Florian Brück, Jean-David Fermanian, Aleksey Min, 2025.
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- Statistical field theory for Markov decision processes under uncertainty
- George Stamatescu, 2025.
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- Bayesian Data Sketching for Varying Coefficient Regression Models
- Rajarshi Guhaniyogi, Laura Baracaldo, Sudipto Banerjee, 2025.
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- Bagged k-Distance for Mode-Based Clustering Using the Probability of Localized Level Sets
- Hanyuan Hang, 2025.
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- Linear cost and exponentially convergent approximation of Gaussian Matérn processes on intervals
- David Bolin, Vaibhav Mehandiratta, Alexandre B. Simas, 2025.
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- Invariant Subspace Decomposition
- Margherita Lazzaretto, Jonas Peters, Niklas Pfister, 2025.
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- Posterior Concentrations of Fully-Connected Bayesian Neural Networks with General Priors on the Weights
- Insung Kong, Yongdai Kim, 2025.
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- Outlier Robust and Sparse Estimation of Linear Regression Coefficients
- Takeyuki Sasai, Hironori Fujisawa, 2025.
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- Affine Rank Minimization via Asymptotic Log-Det Iteratively Reweighted Least Squares
- Sebastian Krämer, 2025.
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- Causal Effect of Functional Treatment
- Ruoxu Tan, Wei Huang, Zheng Zhang, Guosheng Yin, 2025.
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- Uplift Model Evaluation with Ordinal Dominance Graphs
- Brecht Verbeken, Marie-Anne Guerry, Wouter Verbeke, Sam Verboven, 2025.
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- High-Dimensional L2-Boosting: Rate of Convergence
- Ye Luo, Martin Spindler, Jannis Kueck, 2025.
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- Feature Learning in Finite-Width Bayesian Deep Linear Networks with Multiple Outputs and Convolutional Layers
- Federico Bassetti, Marco Gherardi, Alessandro Ingrosso, Mauro Pastore, Pietro Rotondo, 2025.
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- How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences
- Miko{\l}aj J. Kasprzak, Ryan Giordano, Tamara Broderick, 2025.
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- Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Test
- Seunghoon Paik, Michael Celentano, Alden Green, Ryan J. Tibshirani, 2025.
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- On Inference for the Support Vector Machine
- Jakub Rybak, Heather Battey, Wen-Xin Zhou, 2025.
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- Random Pruning Over-parameterized Neural Networks Can Improve Generalization: A Training Dynamics Analysis
- Hongru Yang, Yingbin Liang, Xiaojie Guo, Lingfei Wu, Zhangyang Wang, 2025.
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- Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability
- Atticus Geiger, Duligur Ibeling, Amir Zur, Maheep Chaudhary, Sonakshi Chauhan, Jing Huang, Aryaman Arora, Zhengxuan Wu, Noah Goodman, Christopher Potts, Thomas Icard, 2025.
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- Implicit vs Unfolded Graph Neural Networks
- Yongyi Yang, Tang Liu, Yangkun Wang, Zengfeng Huang, David Wipf, 2025.
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- Towards Optimal Branching of Linear and Semidefinite Relaxations for Neural Network Robustness Certification
- Brendon G. Anderson, Ziye Ma, Jingqi Li, Somayeh Sojoudi, 2025.
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- GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia
- Carlo Lucibello, Aurora Rossi, 2025. (Machine Learning Open Source Software Paper)
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- Dynamic angular synchronization under smoothness constraints
- Ernesto Araya, Mihai Cucuringu, Hemant Tyagi, 2025.
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- Derivative-Informed Neural Operator Acceleration of Geometric MCMC for Infinite-Dimensional Bayesian Inverse Problems
- Lianghao Cao, Thomas O'Leary-Roseberry, Omar Ghattas, 2025.
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- Wasserstein F-tests for Frechet regression on Bures-Wasserstein manifolds
- Haoshu Xu, Hongzhe Li, 2025.
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- Distributed Stochastic Bilevel Optimization: Improved Complexity and Heterogeneity Analysis
- Youcheng Niu, Jinming Xu, Ying Sun, Yan Huang, Li Chai, 2025.
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- Learning causal graphs via nonlinear sufficient dimension reduction
- Eftychia Solea, Bing Li, Kyongwon Kim, 2025.
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- On Consistent Bayesian Inference from Synthetic Data
- Ossi Räisä, Joonas Jälkö, Antti Honkela, 2025.
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- Laplace Meets Moreau: Smooth Approximation to Infimal Convolutions Using Laplace's Method
- Ryan J. Tibshirani, Samy Wu Fung, Howard Heaton, Stanley Osher, 2025.
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- Sampling and Estimation on Manifolds using the Langevin Diffusion
- Karthik Bharath, Alexander Lewis, Akash Sharma, Michael V. Tretyakov, 2025.
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- Sharp Bounds for Sequential Federated Learning on Heterogeneous Data
- Yipeng Li, Xinchen Lyu, 2025.
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- Local Linear Recovery Guarantee of Deep Neural Networks at Overparameterization
- Yaoyu Zhang, Leyang Zhang, Zhongwang Zhang, Zhiwei Bai, 2025.
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- Stabilizing Sharpness-Aware Minimization Through A Simple Renormalization Strategy
- Chengli Tan, Jiangshe Zhang, Junmin Liu, Yicheng Wang, Yunda Hao, 2025.
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- Fine-Grained Change Point Detection for Topic Modeling with Pitman-Yor Process
- Feifei Wang, Zimeng Zhao, Ruimin Ye, Xiaoge Gu, Xiaoling Lu, 2025.
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- Deletion Robust Non-Monotone Submodular Maximization over Matroids
- Paul Dütting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam, 2025.
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- Instability, Computational Efficiency and Statistical Accuracy
- Nhat Ho, Koulik Khamaru, Raaz Dwivedi, Martin J. Wainwright, Michael I. Jordan, Bin Yu, 2025.
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- Estimation of Local Geometric Structure on Manifolds from Noisy Data
- Yariv Aizenbud, Barak Sober, 2025.
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- 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. (Machine Learning Open Source Software Paper)
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- Recursive Causal Discovery
- Ehsan Mokhtarian, Sepehr Elahi, Sina Akbari, Negar Kiyavash, 2025.
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- Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings
- Henrik von Kleist, Alireza Zamanian, Ilya Shpitser, Narges Ahmidi, 2025.
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- On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations
- Antoine Godichon-Baggioni, Nicklas Werge, 2025.
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- Determine the Number of States in Hidden Markov Models via Marginal Likelihood
- Yang Chen, Cheng-Der Fuh, Chu-Lan Michael Kao, 2025.
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- Variance-Aware Estimation of Kernel Mean Embedding
- Geoffrey Wolfer, Pierre Alquier, 2025.
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- Scaling ResNets in the Large-depth Regime
- Pierre Marion, Adeline Fermanian, Gérard Biau, Jean-Philippe Vert, 2025.
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- A Comparative Evaluation of Quantification Methods
- Tobias Schumacher, Markus Strohmaier, Florian Lemmerich, 2025.
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- Lightning UQ Box: Uncertainty Quantification for Neural Networks
- Nils Lehmann, Nina Maria Gottschling, Jakob Gawlikowski, Adam J. Stewart, Stefan Depeweg, Eric Nalisnick, 2025. (Machine Learning Open Source Software Paper)
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- 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.
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- Curvature-based Clustering on Graphs
- Yu Tian, Zachary Lubberts, Melanie Weber, 2025.
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- Composite Goodness-of-fit Tests with Kernels
- Oscar Key, Arthur Gretton, François-Xavier Briol, Tamara Fernandez, 2025.
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- 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. (Machine Learning Open Source Software Paper)
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- The Effect of SGD Batch Size on Autoencoder Learning: Sparsity, Sharpness, and Feature Learning
- Nikhil Ghosh, Spencer Frei, Wooseok Ha, Bin Yu, 2025.
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- Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data
- Pan Zhao, Julie Josse, Shu Yang, 2025.
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- DAGs as Minimal I-maps for the Induced Models of Causal Bayesian Networks under Conditioning
- Xiangdong Xie, Jiahua Guo, Yi Sun, 2025.
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- 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.
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- Learning Global Nash Equilibrium in Team Competitive Games with Generalized Fictitious Cross-Play
- Zelai Xu, Chao Yu, Yancheng Liang, Yi Wu, Yu Wang, 2025.
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- Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models
- Xuefeng Gao, Hoang M. Nguyen, Lingjiong Zhu, 2025.
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- Extremal graphical modeling with latent variables via convex optimization
- Sebastian Engelke, Armeen Taeb, 2025.
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- Efficient and Robust Semi-supervised Estimation of Average Treatment Effect with Partially Annotated Treatment and Response
- Jue Hou, Rajarshi Mukherjee, Tianxi Cai, 2025.
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- Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning
- Kuangyu Ding, Jingyang Li, Kim-Chuan Toh, 2025.
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- Optimizing Data Collection for Machine Learning
- Rafid Mahmood, James Lucas, Jose M. Alvarez, Sanja Fidler, Marc T. Law, 2025.
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- Unbalanced Kantorovich-Rubinstein distance, plan, and barycenter on nite spaces: A statistical perspective
- Shayan Hundrieser, Florian Heinemann, Marcel Klatt, Marina Struleva, Axel Munk, 2025.
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- Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding
- Jiajing Zheng, Alexander D'Amour, Alexander Franks, 2025.
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- 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. (Machine Learning Open Source Software Paper)
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- Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming
- Sen Na, Michael Mahoney, 2025.
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- Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds
- Clément Bonet, Lucas Drumetz, Nicolas Courty, 2025.
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- Accelerating optimization over the space of probability measures
- Shi Chen, Qin Li, Oliver Tse, Stephen J. Wright, 2025.
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- Bayesian Multi-Group Gaussian Process Models for Heterogeneous Group-Structured Data
- Didong Li, Andrew Jones, Sudipto Banerjee, Barbara E. Engelhardt, 2025.
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- Orthogonal Bases for Equivariant Graph Learning with Provable k-WL Expressive Power
- Jia He, Maggie Cheng, 2025.
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- Optimal Experiment Design for Causal Effect Identification
- Sina Akbari, Jalal Etesami, Negar Kiyavash, 2025.
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- 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.
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- The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond
- Jiin Woo, Gauri Joshi, Yuejie Chi, 2025.
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- 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. (Machine Learning Open Source Software Paper)
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- The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise
- Shuze Daniel Liu, Shuhang Chen, Shangtong Zhang, 2025.
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- Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick
- Xiyuan Wang, Pan Li, Muhan Zhang, 2025.
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- 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.
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- Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions
- Dapeng Yao, Fangzheng Xie, Yanxun Xu, 2025.
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- Regularizing Hard Examples Improves Adversarial Robustness
- Hyungyu Lee, Saehyung Lee, Ho Bae, Sungroh Yoon, 2025.
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- Random ReLU Neural Networks as Non-Gaussian Processes
- Rahul Parhi, Pakshal Bohra, Ayoub El Biari, Mehrsa Pourya, Michael Unser, 2025.
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- Supervised Learning with Evolving Tasks and Performance Guarantees
- Verónica Álvarez, Santiago Mazuelas, Jose A. Lozano, 2025.
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- Error estimation and adaptive tuning for unregularized robust M-estimator
- Pierre C. Bellec, Takuya Koriyama, 2025.
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- 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.
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- Locally Private Causal Inference for Randomized Experiments
- Yuki Ohnishi, Jordan Awan, 2025.
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- Estimating Network-Mediated Causal Effects via Principal Components Network Regression
- Alex Hayes, Mark M. Fredrickson, Keith Levin, 2025.
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- Selective Inference with Distributed Data
- Sifan Liu, Snigdha Panigrahi, 2025.
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- Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization
- Tianyi Lin, Chi Jin, Michael I. Jordan, 2025.
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- An Axiomatic Definition of Hierarchical Clustering
- Ery Arias-Castro, Elizabeth Coda, 2025.
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- Test-Time Training on Video Streams
- Renhao Wang, Yu Sun, Arnuv Tandon, Yossi Gandelsman, Xinlei Chen, Alexei A. Efros, Xiaolong Wang, 2025.
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- 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.
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- A Random Matrix Approach to Low-Multilinear-Rank Tensor Approximation
- Hugo Lebeau, Florent Chatelain, Romain Couillet, 2025.
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- Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agents
- Marco Pleines, Matthias Pallasch, Frank Zimmer, Mike Preuss, 2025.
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- 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.
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- Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization
- Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis, 2025.
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- 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.
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- 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.
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- Efficiently Escaping Saddle Points in Bilevel Optimization
- Minhui Huang, Xuxing Chen, Kaiyi Ji, Shiqian Ma, Lifeng Lai, 2025.
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