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
- 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.
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- High-Dimensional Analysis of Gradient Flow for Extensive-Width Quadratic Neural Networks
- Simon Martin, Giulio Biroli, Francis Bach, 2026.
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- 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.
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- Near-optimal Delta-convex Estimation of Lipschitz Functions
- Gábor Balázs, 2026.
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- The Sample Complexity of Parameter-Free Stochastic Convex Optimization
- Jared Lawrence, Ari Kalinsky, Hannah Bradfield, Yair Carmon, Oliver Hinder, 2026.
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- End-to-End Deep Learning for Predicting Metric Space-Valued Outputs
- Yidong Zhou, Su I Iao, Hans-Georg Müller, 2026.
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- Graph-based Clustering Revisited: A Relaxation of Kernel k-Means Perspective
- Wenlong Lyu, Yuheng Jia, Hui Liu, Junhui Hou, 2026.
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- Learning to Play Two-Player Perfect-Information Games without Knowledge
- Quentin Cohen-Solal, 2026.
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- Doubly Debiased Robust Subsampling for Transfer Learning
- Tao Wang, Weng Kee Wong, 2026.
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- Abstract Gradient Training: A Unified Certification Framework for Data Poisoning, Unlearning, and Differential Privacy
- Philip Sosnin, Matthew Wicker, Josh Collyer, Calvin Tsay, 2026.
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- Mixing times of data-augmentation Gibbs samplers for high-dimensional probit regression
- Filippo Ascolani, Giacomo Zanella, 2026.
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- Underdamped Langevin MCMC with third order convergence
- Maximilian Scott, Dáire O'Kane, Andraž Jelinčič, James Foster, 2026.
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- Approximation-Free Differentiable Oblique Decision Trees
- Subrat Prasad Panda, Blaise Genest, Arvind Easwaran, 2026.
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- Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes
- Ruiqi Zhang, Jingfeng Wu, Licong Lin, Peter L. Bartlett, 2026.
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- Adaptive Nonparametric Perturbations of Parametric Models with Generalized Bayes
- Bohan Wu, Eli N. Weinstein, Sohrab Salehi, Yixin Wang, David M. Blei, 2026.
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- 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.
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- Gradient Span Algorithms Make Predictable Progress in High Dimension
- Felix Benning, Leif Döring, 2026.
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- 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.
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- 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.
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- Accelerating Constrained Sampling: A Large Deviations Approach
- Yingli Wang, Changwei Tu, Xiaoyu Wang, Lingjiong Zhu, 2026.
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- Learning general conditional independence structures via the neighbourhood lattice
- Arash A. Amini, Bryon Aragam, Qing Zhou, 2026.
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- Statistical guarantees for denoising reflected diffusion models
- Asbjørn Holk, Claudia Strauch, Lukas Trottner, 2026.
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- Vecchia-Inducing-Points Full-Scale Approximations for Gaussian Processes
- Tim Gyger, Reinhard Furrer, Fabio Sigrist, 2026.
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- STDE++: Polynomial-Time Amortization for Linear Differential Operators
- Zekun Shi, Zheyuan Hu, Min Lin, Kenji Kawaguchi, 2026.
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- The Within-Orbit Adaptive Leapfrog No-U-Turn Sampler
- Nawaf Bou-Rabee, Bob Carpenter, Tore Selland Kleppe, Sifan Liu, 2026.
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- Finite-Time Decoupled Convergence in Nonlinear Two-Time-Scale Stochastic Approximation
- Yuze Han, Xiang Li, Zhihua Zhang, 2026.
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- Embedding Network Autoregression for Time Series Analysis and Causal Peer Effect Inference
- Jae Ho Chang, Subhadeep Paul, 2026.
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- Three Types of Calibration using Properties and their Semantic and Formal Relationships
- Rabanus Derr, Jessie Finocchiaro, Robert C. Williamson, 2026.
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- Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex Optimization
- Siyuan Zhang, Nachuan Xiao, Xin Liu, 2026.
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- A Two-Timescale Primal-Dual Framework for Reinforcement Learning via Online Dual Variable Guidance
- Axel F. Wolter, Tobias Sutter, 2026.
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- FLAGG: Flexible Autoregressive Graph Generation
- Samuel Cognolato, Alessandro Sperduti, Luciano Serafini, 2026.
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- A Unified Approach to Analysis and Design of Denoising Markov Models
- Yinuo Ren, Grant M. Rotskoff, Lexing Ying, 2026.
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- Flavors of Margin: Implicit Bias of Steepest Descent in Homogeneous Neural Networks
- Nikolaos Tsilivis, Eitan Gronich, Julia Kempe, Gal Vardi, 2026.
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- A Single-Loop Stochastic Proximal Quasi-Newton Method for Large-Scale Nonsmooth Convex Optimization
- Yongcun Song, Zimeng Wang, Xiaoming Yuan, Hangrui Yue, 2026.
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- Statistical Learning Theory for Neural Operators
- Niklas Reinhardt, Sven Wang, Jakob Zech, 2026.
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- Deconvolution in unlinked linear models
- Fadoua Balabdaoui, Antonio Di Noia, Cécile Durot, 2026.
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- Spectral Truncation Kernels: Noncommutativity in C*-algebraic Kernel Machines
- Yuka Hashimoto, Ayoub Hafid, Masahiro Ikeda, Hachem Kadri, 2026.
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- High-dimensional Parameter Transfer With Fused-Regularizer
- Zelin He, Ying Sun, Jingyuan Liu, Runze Li, 2026.
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- Exogenous Randomness Empowering Random Forests
- Tianxing Mei, Yingying Fan, Jinchi Lv, 2026.
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- Kernel Mean Embedding Deviation Subspace for Unsupervised Learning with Heterogeneous Data
- Luoyao Yu, Lixing Zhu, Ruoqing Zhu, Xuehu Zhu, 2026.
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- Deep Nonparametric Conditional Independence Tests for Images
- Marco Simnacher, Xiangnan Xu, Hani Park, Christoph Lippert, Sonja Greven, 2026.
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- Semi-supervised learning for linear extremile regression
- Rong Jiang, Jiangfeng Wang, Keming Yu, 2026.
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- Transfer Learning via Regularized Random-effects Linear Discriminant Analysis
- Hongzhe Zhang, Arnab Auddy, Hongzhe Li, 2026.
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- Cheap Bootstrap for Fast Uncertainty Quantification of Stochastic Gradient Descent
- Henry Lam, Zitong Wang, 2026.
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- A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equation
- Shu Liu, Stanley Osher, Wuchen Li, 2026.
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- Generalized Resubstitution for Regression Error Estimation
- Diego Marcondes, Ulisses Braga-Neto, 2026.
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- Transfer Conformal Predictive Inference for Regression
- Ce Zhang, Ting Li, Jinhan Xie, Linglong Kong, Bei Jiang, 2026.
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- Towards Convexity in Anomaly Detection: A New Formulation of SSLM with Unique Optimal Solutions
- Hongying Liu, Hao Wang, Haoran Chu, Yibo Wu, 2026.
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- Node Regression on Latent Position Random Graphs via Local Averaging
- Martin Gjorgjevski, Nicolas Keriven, Simon Barthelme, Yohann De Castro, 2026.
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- On the Relevance of Byzantine Robust Optimization Against Data Poisoning
- Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, 2026.
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- Best Arm Identification with Minimal Regret
- Junwen Yang, Vincent Y. F. Tan, Tianyuan Jin, 2026.
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- Convergence of Noise-Free Sampling Algorithms with Regularized Wasserstein Proximals
- Fuqun Han, Stanley Osher, Wuchen Li, 2026.
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- Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features
- Jiuqi Wang, Shangtong Zhang, 2026.
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- Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models
- Jiaqi Li, Johannes Schmidt-Hieber, Wei Biao Wu, 2026.
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- Beyond Unconstrained Features: Neural Collapse for Shallow Neural Networks with General Data
- Wanli Hong, Shuyang Ling, 2026.
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- Differentially Private Estimation and Inference in High-Dimensional Regression with FDR Control
- Zhanrui Cai, Sai Li, Xintao Xia, Linjun Zhang, 2026.
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- Demographic Parity in Regression and Classification Within the Unawareness Framework
- Vincent Divol, Solenne Gaucher, 2026.
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- Enhancing Accuracy in Generative Models via Knowledge Transfer
- Xinyu Tian, Xiaotong Shen, 2026.
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- Multi-relational Network Autoregression Model with Latent Group Structures
- Yimeng Ren, Xuening Zhu, Ganggang Xu, Yanyuan Ma, 2026.
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- Limiting Over-Smoothing and Over-Squashing of Graph Message Passing by Deep Scattering Transforms
- Yuanhong Jiang, Dongmian Zou, Xiaoqun Zhang, Yu Guang Wang, 2026.
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- A Fully Parameter-Free Second-Order Algorithm for Convex-Concave Minimax Problems
- Jun-Lin Wang, Zi Xu, Hui-Ling Zhang, 2026.
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- Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent
- Adrien Schertzer, Loucas Pillaud-Vivien, 2026.
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- Vector-Valued Gaussian Processes for Approximating Divergence- or Rotation-free Vector Fields
- Quoc Thong Le Gia, Ian Hugh Sloan, Holger Wendland, 2026.
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- Differentially Private Best-Arm Identification
- Achraf Azize, Marc Jourdan, Aymen Al Marjani, Debabrota Basu, 2026.
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- Corruptions of Supervised Learning Problems: Typology and Mitigations
- Laura Iacovissi, Nan Lu, Robert C. Williamson, 2026.
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- 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.
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- 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.
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- Minimax density estimation in the adversarial framework under local differential privacy
- Mélisande Albert, Juliette Chevallier, Béatrice Laurent, Ousmane Sacko, 2026.
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- Approximations and Learning for Continuous State and Action MDPs under Average Cost Criteria
- Ali D. Kara, Serdar Yüksel, 2026.
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- Optimal Approximation and Generalization Errors for Deep Convolutional Neural Networks
- Jinxin Wang, Shao-Bo Lin, 2026.
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- Investigating the Histogram Loss in Regression
- Ehsan Imani, Kai Luedemann, Sam Scholnick-Hughes, Esraa Elelimy, Martha White, 2026.
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- Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing
- Xianli Zeng, Kevin Jiang, Guang Cheng, Edgar Dobriban, 2026.
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- Why "Classic" Transformers Are Shallow and A Depth-Enabling Technique
- Yueyao Yu, Yin Zhang, 2026.
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- A Convex Framework for Confounding Robust Inference
- Kei Ishikawa, Niao He, Takafumi Kanamori, 2026.
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- Global Fréchet Manifold Learning for Random Objects, With Application to Low-Dimensional Wasserstein Representations of Distributional Data
- Álvaro Gajardo, Hans-Georg Müller, 2026.
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- Probabilistic Rainfall Downscaling: Joint Generalized Neural Models with Censored Spatial Gaussian Copula
- David Huk, Rilwan A. Adewoyin, Ritabrata Dutta, 2026.
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- Sparse Topic Modeling via Spectral Decomposition and Thresholding
- Huy Tran, Yating Liu, Claire Donnat, 2026.
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- Nonparametric generative modeling for time series via Schrödinger bridge
- Mohamed Hamdouche, Pierre Henry-Labordère, Huyên Pham, 2026.
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- 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.
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- Neural Exploitation and Exploration of Contextual Bandits
- Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He, 2026.
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- Knowledge Cascade: Reverse Knowledge Distillation on Nonparametric Multivariate Functional Estimation
- Luyang Fang, Haoran Lu, Yongkai Chen, Wenxuan Zhong, Ping Ma, 2026.
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- Inference with non-differentiable surrogate loss in a general high-dimensional classification framework
- Muxuan Liang, Yang Ning, Maureen A Smith, Ying-Qi Zhao, 2026.
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- A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks
- Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna, 2026.
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- The Role of Contextual Information in Best Arm Identification
- Masahiro Kato, Kaito Ariu, 2026.
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- Transformers Can Overcome the Curse of Dimensionality: A Theoretical Study from an Approximation Perspective
- Yuling Jiao, Yanming Lai, Yang Wang, Bokai Yan, 2026.
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- Covariate-dependent Hierarchical Dirichlet Processes
- Huizi Zhang, Sara Wade, Natalia Bochkina, 2026.
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- DCatalyst: A Unified Accelerated Framework for Decentralized Optimization
- TIanyu Cao, Xiaokai Chen, Gesualdo Scutari, 2026.
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- Boosted Control Functions: Distribution Generalization and Invariance in Confounded Models
- Nicola Gnecco, Jonas Peters, Sebastian Engelke, Niklas Pfister, 2026.
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- 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.
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- A Symplectic Analysis of Alternating Mirror Descent
- Jonas E. Katona, Xiuyuan Wang, Andre Wibisono, 2026.
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- Two-way Node Popularity Model for Directed and Bipartite Networks
- Bing-Yi Jing, Ting Li, Jiangzhou Wang, Ya Wang, 2026.
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- Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization
- Yuchen Li, Laura Balzano, Deanna Needell, Hanbaek Lyu, 2026.
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- Bayesian Inference of Contextual Bandit Policies via Empirical Likelihood
- Jiangrong Ouyang, Mingming Gong, Howard Bondell, 2026.
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- A causal fused lasso for interpretable heterogeneous treatment effects estimation
- Oscar Hernan Madrid Padilla, Yanzhen Chen, Carlos Misael Madrid Padilla, Gabriel Ruiz, 2026.
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- Unsupervised Feature Selection via Nonnegative Orthogonal Constrained Regularized Minimization
- Yan Li, Defeng Sun, Liping Zhang, 2026.
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- 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.
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- Optimizing Attention with Mirror Descent: Generalized Max-Margin Token Selection
- Addison Kristanto Julistiono, Davoud Ataee Tarzanagh, Navid Azizan, 2026.
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- Adaptive Forward Stepwise: A Method for High Sparsity Regression
- Ivy Zhang, Robert Tibshirani, 2026.
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- Optimization and Generalization of Gradient Descent for Shallow ReLU Networks with Minimal Width
- Yunwen Lei, Puyu Wang, Yiming Ying, Ding-Xuan Zhou, 2026.
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- Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection
- Steven Adams, Andrea Patanè, Morteza Lahijanian, Luca Laurenti, 2026.
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- CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration
- Sophie Jaffard, Samuel Vaiter, Patricia Reynaud-Bouret, 2026.
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- Persistence Diagrams Estimation of Multivariate Piecewise Hölder-continuous Signals
- Hugo Henneuse, 2026.
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- Exploring Novel Uncertainty Quantification through Forward Intensity Function Modeling
- Yudong Wang, Zhi-Sheng Ye, Cheng Yong Tang, 2026.
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- Communication-efficient Distributed Statistical Inference for Massive Data with Heterogeneous Auxiliary Information
- Miaomiao Yu, Zhongfeng Jiang, Jiaxuan Li, Yong Zhou, 2026.
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- Decorrelated Local Linear Estimator: Inference for Non-linear Effects in High-dimensional Additive Models
- Zijian Guo, Wei Yuan, Cunhui Zhang, 2026.
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- Refined Risk Bounds for Unbounded Losses via Transductive Priors
- Jian Qian, Alexander Rakhlin, Nikita Zhivotovskiy, 2026.
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- A Common Interface for Automatic Differentiation
- Guillaume Dalle, Adrian Hill, 2026. (Machine Learning Open Source Software Paper)
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- 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.
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- The Distribution of Ridgeless Least Squares Interpolators
- Qiyang Han, Xiaocong Xu, 2026.
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- Nonparametric Estimation of a Factorizable Density using Diffusion Models
- Hyeok Kyu Kwon, Dongha Kim, Ilsang Ohn, Minwoo Chae, 2026.
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- Learning Bayesian Network Classifiers to Minimize Class Variable Parameters
- Shouta Sugahara, Koya Kato, James Cussens, Maomi Ueno, 2026.
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- Simulation-based Calibration of Uncertainty Intervals under Approximate Bayesian Estimation
- Terrance D. Savitsky, Julie Gershunskaya, 2026.
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- An Anytime Algorithm for Good Arm Identification
- Marc Jourdan, Andrée Delahaye-Duriez, Clémence Réda, 2026.
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- Extrapolated Markov Chain Oversampling Method for Imbalanced Text Classification
- Aleksi Avela, Pauliina Ilmonen, 2026.
[abs][pdf][bib] [code]
- Neural Network Parameter-optimization of Gaussian Pre-marginalized Directed Acyclic Graphs
- Mehrzad Saremi, 2026. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]
- Flexible Functional Treatment Effect Estimation
- Jiayi Wang, Raymond K. W. Wong, Xiaoke Zhang, Kwun Chuen Gary Chan, 2026.
[abs][pdf][bib]
- Error Analysis for Deep ReLU Feedforward Density-Ratio Estimation with Bregman Divergence
- Siming Zheng, Guohao Shen, Yuanyuan Lin, Jian Huang, 2026.
[abs][pdf][bib]
- A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design
- Rui Ai, Boxiang Lyu, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan, 2026.
[abs][pdf][bib] [code]
- 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. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]
- Nonlocal Techniques for the Analysis of Deep ReLU Neural Network Approximations
- Cornelia Schneider, Mario Ullrich, Jan Vybíral, 2026.
[abs][pdf][bib]
- A Data-Augmented Contrastive Learning Approach to Nonparametric Density Estimation
- Chenghao Li, Yuanyuan Lin, 2026.
[abs][pdf][bib] [code]
- Guaranteed Nonconvex Low-Rank Tensor Estimation via Scaled Gradient Descent
- Tong Wu, 2026.
[abs][pdf][bib] [code]
- skwdro: a library for Wasserstein distributionally robust machine learning
- Vincent Florian, Waïss Azizian, Franck Iutzeler, Jérôme Malick, 2026. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]
- Extending Mean-Field Variational Inference via Entropic Regularization: Theory and Computation
- Bohan Wu, David M. Blei, 2026.
[abs][pdf][bib]
- Stochastic Gradient Methods: Bias, Stability and Generalization
- Shuang Zeng, Yunwen Lei, 2026.
[abs][pdf][bib]
- Classification Under Local Differential Privacy with Model Reversal and Model Averaging
- Caihong Qin, Yang Bai, 2026.
[abs][pdf][bib]
- 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.
[abs][pdf][bib] [code]
- Efficient frequent directions algorithms for approximate decomposition of matrices and higher-order tensors
- Maolin Che, Yimin Wei, Hong Yan, 2026.
[abs][pdf][bib]
- Online Detection of Changes in Moment--Based Projections: When to Retrain Deep Learners or Update Portfolios?
- Ansgar Steland, 2026.
[abs][pdf][bib]
- 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.
[abs][pdf][bib] [code]
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