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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.


Latest papers

Deep Network Approximation: Beyond ReLU to Diverse Activation Functions
Shijun Zhang, Jianfeng Lu, Hongkai Zhao, 2024.

Effect-Invariant Mechanisms for Policy Generalization
Sorawit Saengkyongam, Niklas Pfister, Predrag Klasnja, Susan Murphy, Jonas Peters, 2024.

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)
[abs][pdf][bib]      [code]

Heterogeneous-Agent Reinforcement Learning
Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng, Siyi Hu, Jiaming Ji, Yaodong Yang, 2024.
[abs][pdf][bib]      [code]

Sample-efficient Adversarial Imitation Learning
Dahuin Jung, Hyungyu Lee, Sungroh Yoon, 2024.

Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent
Benjamin Gess, Sebastian Kassing, Vitalii Konarovskyi, 2024.

Rates of convergence for density estimation with generative adversarial networks
Nikita Puchkin, Sergey Samsonov, Denis Belomestny, Eric Moulines, Alexey Naumov, 2024.

Additive smoothing error in backward variational inference for general state-space models
Mathis Chagneux, Elisabeth Gassiat, Pierre Gloaguen, Sylvain Le Corff, 2024.

Optimal Bump Functions for Shallow ReLU networks: Weight Decay, Depth Separation, Curse of Dimensionality
Stephan Wojtowytsch, 2024.

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.
[abs][pdf][bib]      [code]

On Tail Decay Rate Estimation of Loss Function Distributions
Etrit Haxholli, Marco Lorenzi, 2024.
[abs][pdf][bib]      [code]

Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces
Hao Liu, Haizhao Yang, Minshuo Chen, Tuo Zhao, Wenjing Liao, 2024.

Post-Regularization Confidence Bands for Ordinary Differential Equations
Xiaowu Dai, Lexin Li, 2024.

On the Generalization of Stochastic Gradient Descent with Momentum
Ali Ramezani-Kebrya, Kimon Antonakopoulos, Volkan Cevher, Ashish Khisti, Ben Liang, 2024.

Pursuit of the Cluster Structure of Network Lasso: Recovery Condition and Non-convex Extension
Shotaro Yagishita, Jun-ya Gotoh, 2024.

Iterate Averaging in the Quest for Best Test Error
Diego Granziol, Nicholas P. Baskerville, Xingchen Wan, Samuel Albanie, Stephen Roberts, 2024.
[abs][pdf][bib]      [code]

Nonparametric Inference under B-bits Quantization
Kexuan Li, Ruiqi Liu, Ganggang Xu, Zuofeng Shang, 2024.

Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box
Ryan Giordano, Martin Ingram, Tamara Broderick, 2024.
[abs][pdf][bib]      [code]

On Sufficient Graphical Models
Bing Li, Kyongwon Kim, 2024.

Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond
Nathan Kallus, Xiaojie Mao, Masatoshi Uehara, 2024.
[abs][pdf][bib]      [code]

On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks
Sebastian Neumayer, Lénaïc Chizat, Michael Unser, 2024.

Improving physics-informed neural networks with meta-learned optimization
Alex Bihlo, 2024.

A Comparison of Continuous-Time Approximations to Stochastic Gradient Descent
Stefan Ankirchner, Stefan Perko, 2024.

Critically Assessing the State of the Art in Neural Network Verification
Matthias König, Annelot W. Bosman, Holger H. Hoos, Jan N. van Rijn, 2024.

Estimating the Minimizer and the Minimum Value of a Regression Function
Arya Akhava, Davit Gogolashvili, Alexandre B. Tsybakov, 2024.

Modeling Random Networks with Heterogeneous Reciprocity
Daniel Cirkovic, Tiandong Wang, 2024.

Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment
Zixian Yang, Xin Liu, Lei Ying, 2024.

On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models
Yangjing Zhang, Ying Cui, Bodhisattva Sen, Kim-Chuan Toh, 2024.
[abs][pdf][bib]      [code]

Decorrelated Variable Importance
Isabella Verdinelli, Larry Wasserman, 2024.

Model-Free Representation Learning and Exploration in Low-Rank MDPs
Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, 2024.

Seeded Graph Matching for the Correlated Gaussian Wigner Model via the Projected Power Method
Ernesto Araya, Guillaume Braun, Hemant Tyagi, 2024.
[abs][pdf][bib]      [code]

Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization
Shicong Cen, Yuting Wei, Yuejie Chi, 2024.

Power of knockoff: The impact of ranking algorithm, augmented design, and symmetric statistic
Zheng Tracy Ke, Jun S. Liu, Yucong Ma, 2024.

Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction
Yuze Han, Guangzeng Xie, Zhihua Zhang, 2024.

On Truthing Issues in Supervised Classification
Jonathan K. Su, 2024.

Full list

© JMLR 2024.