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
- 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
- Trained Transformers Learn Linear Models In-Context
- Ruiqi Zhang, Spencer Frei, Peter L. Bartlett, 2024.
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- Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees
- Nachuan Xiao, Xiaoyin Hu, Xin Liu, Kim-Chuan Toh, 2024.
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- Efficient Modality Selection in Multimodal Learning
- Yifei He, Runxiang Cheng, Gargi Balasubramaniam, Yao-Hung Hubert Tsai, Han Zhao, 2024.
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- A Multilabel Classification Framework for Approximate Nearest Neighbor Search
- Ville Hyvönen, Elias Jääsaari, Teemu Roos, 2024.
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- Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization
- Lorenzo Pacchiardi, Rilwan A. Adewoyin, Peter Dueben, Ritabrata Dutta, 2024.
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- Multiple Descent in the Multiple Random Feature Model
- Xuran Meng, Jianfeng Yao, Yuan Cao, 2024.
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- Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling
- Ye He, Tyler Farghly, Krishnakumar Balasubramanian, Murat A. Erdogdu, 2024.
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- Invariant and Equivariant Reynolds Networks
- Akiyoshi Sannai, Makoto Kawano, Wataru Kumagai, 2024. (Machine Learning Open Source Software Paper)
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- Personalized PCA: Decoupling Shared and Unique Features
- Naichen Shi, Raed Al Kontar, 2024.
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- Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee
- George H. Chen, 2024.
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- On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control
- Amrit Singh Bedi, Anjaly Parayil, Junyu Zhang, Mengdi Wang, Alec Koppel, 2024.
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- Convergence for nonconvex ADMM, with applications to CT imaging
- Rina Foygel Barber, Emil Y. Sidky, 2024.
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- Distributed Gaussian Mean Estimation under Communication Constraints: Optimal Rates and Communication-Efficient Algorithms
- T. Tony Cai, Hongji Wei, 2024.
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- Sparse NMF with Archetypal Regularization: Computational and Robustness Properties
- Kayhan Behdin, Rahul Mazumder, 2024.
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- Deep Network Approximation: Beyond ReLU to Diverse Activation Functions
- Shijun Zhang, Jianfeng Lu, Hongkai Zhao, 2024.
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- Effect-Invariant Mechanisms for Policy Generalization
- Sorawit Saengkyongam, Niklas Pfister, Predrag Klasnja, Susan Murphy, Jonas Peters, 2024.
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- Pygmtools: A Python Graph Matching Toolkit
- Runzhong Wang, Ziao Guo, Wenzheng Pan, Jiale Ma, Yikai Zhang, Nan Yang, Qi Liu, Longxuan Wei, Hanxue Zhang, Chang Liu, Zetian Jiang, Xiaokang Yang, Junchi Yan, 2024. (Machine Learning Open Source Software Paper)
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- Heterogeneous-Agent Reinforcement Learning
- Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng, Siyi Hu, Jiaming Ji, Yaodong Yang, 2024.
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- Sample-efficient Adversarial Imitation Learning
- Dahuin Jung, Hyungyu Lee, Sungroh Yoon, 2024.
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- Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent
- Benjamin Gess, Sebastian Kassing, Vitalii Konarovskyi, 2024.
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- Rates of convergence for density estimation with generative adversarial networks
- Nikita Puchkin, Sergey Samsonov, Denis Belomestny, Eric Moulines, Alexey Naumov, 2024.
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- Additive smoothing error in backward variational inference for general state-space models
- Mathis Chagneux, Elisabeth Gassiat, Pierre Gloaguen, Sylvain Le Corff, 2024.
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- Optimal Bump Functions for Shallow ReLU networks: Weight Decay, Depth Separation, Curse of Dimensionality
- Stephan Wojtowytsch, 2024.
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- Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
- Alexander Terenin, David R. Burt, Artem Artemev, Seth Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge, 2024.
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- On Tail Decay Rate Estimation of Loss Function Distributions
- Etrit Haxholli, Marco Lorenzi, 2024.
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- Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces
- Hao Liu, Haizhao Yang, Minshuo Chen, Tuo Zhao, Wenjing Liao, 2024.
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- Post-Regularization Confidence Bands for Ordinary Differential Equations
- Xiaowu Dai, Lexin Li, 2024.
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- On the Generalization of Stochastic Gradient Descent with Momentum
- Ali Ramezani-Kebrya, Kimon Antonakopoulos, Volkan Cevher, Ashish Khisti, Ben Liang, 2024.
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- Pursuit of the Cluster Structure of Network Lasso: Recovery Condition and Non-convex Extension
- Shotaro Yagishita, Jun-ya Gotoh, 2024.
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- Iterate Averaging in the Quest for Best Test Error
- Diego Granziol, Nicholas P. Baskerville, Xingchen Wan, Samuel Albanie, Stephen Roberts, 2024.
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- Nonparametric Inference under B-bits Quantization
- Kexuan Li, Ruiqi Liu, Ganggang Xu, Zuofeng Shang, 2024.
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- Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box
- Ryan Giordano, Martin Ingram, Tamara Broderick, 2024.
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- Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond
- Nathan Kallus, Xiaojie Mao, Masatoshi Uehara, 2024.
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- On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks
- Sebastian Neumayer, Lénaïc Chizat, Michael Unser, 2024.
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- Improving physics-informed neural networks with meta-learned optimization
- Alex Bihlo, 2024.
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- A Comparison of Continuous-Time Approximations to Stochastic Gradient Descent
- Stefan Ankirchner, Stefan Perko, 2024.
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- Critically Assessing the State of the Art in Neural Network Verification
- Matthias König, Annelot W. Bosman, Holger H. Hoos, Jan N. van Rijn, 2024.
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- Estimating the Minimizer and the Minimum Value of a Regression Function under Passive Design
- Arya Akhavan, Davit Gogolashvili, Alexandre B. Tsybakov, 2024.
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- Modeling Random Networks with Heterogeneous Reciprocity
- Daniel Cirkovic, Tiandong Wang, 2024.
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- Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment
- Zixian Yang, Xin Liu, Lei Ying, 2024.
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- On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models
- Yangjing Zhang, Ying Cui, Bodhisattva Sen, Kim-Chuan Toh, 2024.
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- Model-Free Representation Learning and Exploration in Low-Rank MDPs
- Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, 2024.
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- Seeded Graph Matching for the Correlated Gaussian Wigner Model via the Projected Power Method
- Ernesto Araya, Guillaume Braun, Hemant Tyagi, 2024.
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- Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization
- Shicong Cen, Yuting Wei, Yuejie Chi, 2024.
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- Power of knockoff: The impact of ranking algorithm, augmented design, and symmetric statistic
- Zheng Tracy Ke, Jun S. Liu, Yucong Ma, 2024.
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- Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction
- Yuze Han, Guangzeng Xie, Zhihua Zhang, 2024.
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