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

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

Inherent Tradeoffs in Learning Fair Representations: Han Zhao, Geoffrey J. Gordon, 2022.
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solo-learn: A Library of Self-supervised Methods for Visual Representation Learning: Victor Guilherme Turrisi da Costa, Enrico Fini, Moin Nabi, Nicu Sebe, Elisa Ricci, 2022. (Machine Learning Open Source Software Paper)
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Bayesian Pseudo Posterior Mechanism under Asymptotic Differential Privacy: Terrance D. Savitsky, Matthew R.Williams, Jingchen Hu, 2022.
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SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization: Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter, 2022. (Machine Learning Open Source Software Paper)
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DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python: Philipp Bach, Victor Chernozhukov, Malte S. Kurz, Martin Spindler, 2022. (Machine Learning Open Source Software Paper)
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LinCDE: Conditional Density Estimation via Lindsey's Method: Zijun Gao, Trevor Hastie, 2022.
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Toolbox for Multimodal Learn (scikit-multimodallearn): Dominique Benielli, Baptiste Bauvin, Sokol Koço, Riikka Huusari, Cécile Capponi, Hachem Kadri, François Laviolette, 2022. (Machine Learning Open Source Software Paper)
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Analytically Tractable Hidden-States Inference in Bayesian Neural Networks: Luong-Ha Nguyen, James-A. Goulet, 2022.
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Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection: Xinyi Wang, Lang Tong, 2022.
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Overparameterization of Deep ResNet: Zero Loss and Mean-field Analysis: Zhiyan Ding, Shi Chen, Qin Li, Stephen J. Wright, 2022.
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Cascaded Diffusion Models for High Fidelity Image Generation: Jonathan Ho, Chitwan Saharia, William Chan, David J. Fleet, Mohammad Norouzi, Tim Salimans, 2022.
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Beyond Sub-Gaussian Noises: Sharp Concentration Analysis for Stochastic Gradient Descent: Wanrong Zhu, Zhipeng Lou, Wei Biao Wu, 2022.
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Optimal Transport for Stationary Markov Chains via Policy Iteration: Kevin O'Connor, Kevin McGoff, Andrew B. Nobel, 2022.
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PAC Guarantees and Effective Algorithms for Detecting Novel Categories: Si Liu, Risheek Garrepalli, Dan Hendrycks, Alan Fern, Debashis Mondal, Thomas G. Dietterich, 2022.
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Sampling Permutations for Shapley Value Estimation: Rory Mitchell, Joshua Cooper, Eibe Frank, Geoffrey Holmes, 2022.
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Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks: Zhong Li, Jiequn Han, Weinan E, Qianxiao Li, 2022.
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The correlation-assisted missing data estimator: Timothy I. Cannings, Yingying Fan, 2022.
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Structure-adaptive Manifold Estimation: Nikita Puchkin, Vladimir Spokoiny, 2022.
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(f,Gamma)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics: Jeremiah Birrell, Paul Dupuis, Markos A. Katsoulakis, Yannis Pantazis, Luc Rey-Bellet, 2022.
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Score Matched Neural Exponential Families for Likelihood-Free Inference: Lorenzo Pacchiardi, Ritabrata Dutta, 2022.
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Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric: Matteo Pegoraro, Mario Beraha, 2022.
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Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization: Feihu Huang, Shangqian Gao, Jian Pei, Heng Huang, 2022.
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Optimality and Stability in Non-Convex Smooth Games: Guojun Zhang, Pascal Poupart, Yaoliang Yu, 2022.
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SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks: Weijing Tang, Jiaqi Ma, Qiaozhu Mei, Ji Zhu, 2022.
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Model Averaging Is Asymptotically Better Than Model Selection For Prediction: Tri M. Le, Bertrand S. Clarke, 2022.
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Active Learning for Nonlinear System Identification with Guarantees: Horia Mania, Michael I. Jordan, Benjamin Recht, 2022.
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An improper estimator with optimal excess risk in misspecified density estimation and logistic regression: Jaouad Mourtada, Stéphane Gaïffas, 2022.
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A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One: Augusto Fasano, Daniele Durante, 2022.
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Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning: Kaiyi Ji, Junjie Yang, Yingbin Liang, 2022.
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Novel Min-Max Reformulations of Linear Inverse Problems: Mohammed Rayyan Sheriff, Debasish Chatterjee, 2022.
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Data-Derived Weak Universal Consistency: Narayana Santhanam, Venkatachalam Anantharam, Wojciech Szpankowski, 2022.
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MurTree: Optimal Decision Trees via Dynamic Programming and Search: Emir Demirović, Anna Lukina, Emmanuel Hebrard, Jeffrey Chan, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao, Peter J. Stuckey, 2022.
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Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting: Maxime Vono, Daniel Paulin, Arnaud Doucet, 2022.
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On Biased Stochastic Gradient Estimation: Derek Driggs, Jingwei Liang, Carola-Bibiane Schönlieb, 2022.
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Fast and Robust Rank Aggregation against Model Misspecification: Yuangang Pan, Ivor W. Tsang, Weijie Chen, Gang Niu, Masashi Sugiyama, 2022.
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LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data: Ali Eshragh, Fred Roosta, Asef Nazari, Michael W. Mahoney, 2022.
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Evolutionary Variational Optimization of Generative Models: Jakob Drefs, Enrico Guiraud, Jörg Lücke, 2022.
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Supervised Dimensionality Reduction and Visualization using Centroid-Encoder: Tomojit Ghosh, Michael Kirby, 2022.
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Universal Approximation in Dropout Neural Networks: Oxana A. Manita, Mark A. Peletier, Jacobus W. Portegies, Jaron Sanders, Albert Senen-Cerda, 2022.
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Decimated Framelet System on Graphs and Fast G-Framelet Transforms: Xuebin Zheng, Bingxin Zhou, Yu Guang Wang, Xiaosheng Zhuang, 2022.
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Spatial Multivariate Trees for Big Data Bayesian Regression: Michele Peruzzi, David B. Dunson, 2022.
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TFPnP: Tuning-free Plug-and-Play Proximal Algorithms with Applications to Inverse Imaging Problems: Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang, Ying Fu, Hua Huang, Carola-Bibiane Schönlieb, 2022.
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A Stochastic Bundle Method for Interpolation: Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar, 2022.
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On Generalizations of Some Distance Based Classifiers for HDLSS Data: Sarbojit Roy, Soham Sarkar, Subhajit Dutta, Anil K. Ghosh, 2022.
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Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality: Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet, 2022.
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Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems: Jayakumar Subramanian, Amit Sinha, Raihan Seraj, Aditya Mahajan, 2022.
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Near Optimality of Finite Memory Feedback Policies in Partially Observed Markov Decision Processes: Ali Kara, Serdar Yuksel, 2022.
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Interpolating Predictors in High-Dimensional Factor Regression: Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp, 2022.
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Scaling Laws from the Data Manifold Dimension: Utkarsh Sharma, Jared Kaplan, 2022.
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Deep Learning in Target Space: Michael Fairbank, Spyridon Samothrakis, Luca Citi, 2022.
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Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes: Justin D. Silverman, Kimberly Roche, Zachary C. Holmes, Lawrence A. David, Sayan Mukherjee, 2022.
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XAI Beyond Classification: Interpretable Neural Clustering: Xi Peng, Yunfan Li, Ivor W. Tsang, Hongyuan Zhu, Jiancheng Lv, Joey Tianyi Zhou, 2022.
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Empirical Risk Minimization under Random Censorship: Guillaume Ausset, Stephan Clémençon, François Portier, 2022.
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Exploiting locality in high-dimensional Factorial hidden Markov models: Lorenzo Rimella, Nick Whiteley, 2022.
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Recovering shared structure from multiple networks with unknown edge distributions: Keith Levin, Asad Lodhia, Elizaveta Levina, 2022.
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Debiased Distributed Learning for Sparse Partial Linear Models in High Dimensions: Shaogao Lv, Heng Lian, 2022.
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Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models: Subhabrata Majumdar, George Michailidis, 2022.
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Full list