Journal of Machine Learning Research

The Journal of Machine Learning Research (JMLR) 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

From Low Probability to High Confidence in Stochastic Convex Optimization: Damek Davis, Dmitriy Drusvyatskiy, Lin Xiao, Junyu Zhang, 2021.
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Optimal Feedback Law Recovery by Gradient-Augmented Sparse Polynomial Regression: Behzad Azmi, Dante Kalise, Karl Kunisch, 2021.
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Understanding Recurrent Neural Networks Using Nonequilibrium Response Theory: Soon Hoe Lim, 2021.
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Optimal Structured Principal Subspace Estimation: Metric Entropy and Minimax Rates: Tony Cai, Hongzhe Li, Rong Ma, 2021.
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RaSE: Random Subspace Ensemble Classification: Ye Tian, Yang Feng, 2021.
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Wasserstein barycenters can be computed in polynomial time in fixed dimension: Jason M Altschuler, Enric Boix-Adsera, 2021.
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Banach Space Representer Theorems for Neural Networks and Ridge Splines: Rahul Parhi, Robert D. Nowak, 2021.
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High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm: Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan, 2021.
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From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction: Henning Lange, Steven L. Brunton, J. Nathan Kutz, 2021.
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Residual Energy-Based Models for Text: Anton Bakhtin, Yuntian Deng, Sam Gross, Myle Ott, Marc'Aurelio Ranzato, Arthur Szlam, 2021.
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giotto-tda: : A Topological Data Analysis Toolkit for Machine Learning and Data Exploration: Guillaume Tauzin, Umberto Lupo, Lewis Tunstall, Julian Burella Pérez, Matteo Caorsi, Anibal M. Medina-Mardones, Alberto Dassatti, Kathryn Hess, 2021. (Machine Learning Open Source Software Paper)
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Risk-Averse Learning by Temporal Difference Methods with Markov Risk Measures: Umit Köse, Andrzej Ruszczyński, 2021.
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A Bayesian Contiguous Partitioning Method for Learning Clustered Latent Variables: Zhao Tang Luo, Huiyan Sang, Bani Mallick, 2021.
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Multi-class Gaussian Process Classification with Noisy Inputs: Carlos Villacampa-Calvo, Bryan Zaldívar, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, 2021.
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Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation: Melkior Ornik, Ufuk Topcu, 2021.
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Neighborhood Structure Assisted Non-negative Matrix Factorization and Its Application in Unsupervised Point-wise Anomaly Detection: Imtiaz Ahmed, Xia Ben Hu, Mithun P. Acharya, Yu Ding, 2021.
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Asynchronous Online Testing of Multiple Hypotheses: Tijana Zrnic, Aaditya Ramdas, Michael I. Jordan, 2021.
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Learning interaction kernels in heterogeneous systems of agents from multiple trajectories: Fei Lu, Mauro Maggioni, Sui Tang, 2021.
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FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference: Tianyu Wang, Marco Morucci, M. Usaid Awan, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky, 2021.
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A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms: Oliver Kroemer, Scott Niekum, George Konidaris, 2021.
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Single and Multiple Change-Point Detection with Differential Privacy: Wanrong Zhang, Sara Krehbiel, Rui Tuo, Yajun Mei, Rachel Cummings, 2021.
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Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits: Julian Zimmert, Yevgeny Seldin, 2021.
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Inference In High-dimensional Single-Index Models Under Symmetric Designs: Hamid Eftekhari, Moulinath Banerjee, Ya'acov Ritov, 2021.
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Finite Time LTI System Identification: Tuhin Sarkar, Alexander Rakhlin, Munther A. Dahleh, 2021.
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Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions: Yunwen Lei, Ting Hu, Ke Tang, 2021.
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Entangled Kernels - Beyond Separability: Riikka Huusari, Hachem Kadri, 2021.
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A Two-Level Decomposition Framework Exploiting First and Second Order Information for SVM Training Problems: Giulio Galvan, Matteo Lapucci, Chih-Jen Lin, Marco Sciandrone, 2021.
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When random initializations help: a study of variational inference for community detection: Purnamrita Sarkar, Y. X. Rachel Wang, Soumendu S. Mukherjee, 2021.
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A Fast Globally Linearly Convergent Algorithm for the Computation of Wasserstein Barycenters: Lei Yang, Jia Li, Defeng Sun, Kim-Chuan Toh, 2021.
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Aggregated Hold-Out: Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle, 2021.
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Ranking and synchronization from pairwise measurements via SVD: Alexandre d'Aspremont, Mihai Cucuringu, Hemant Tyagi, 2021.
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A Unified Sample Selection Framework for Output Noise Filtering: An Error-Bound Perspective: Gaoxia Jiang, Wenjian Wang, Yuhua Qian, Jiye Liang, 2021.
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Continuous Time Analysis of Momentum Methods: Nikola B. Kovachki, Andrew M. Stuart, 2021.
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Pykg2vec: A Python Library for Knowledge Graph Embedding: Shih-Yuan Yu, Sujit Rokka Chhetri, Arquimedes Canedo, Palash Goyal, Mohammad Abdullah Al Faruque, 2021. (Machine Learning Open Source Software Paper)
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Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-examples: Jagdeep Singh Bhatia, 2021.
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On Multi-Armed Bandit Designs for Dose-Finding Trials: Maryam Aziz, Emilie Kaufmann, Marie-Karelle Riviere, 2021.
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Homogeneity Structure Learning in Large-scale Panel Data with Heavy-tailed Errors: Xiao Di, Yuan Ke, Runze Li, 2021.
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Global and Quadratic Convergence of Newton Hard-Thresholding Pursuit: Shenglong Zhou, Naihua Xiu, Hou-Duo Qi, 2021.
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Unfolding-Model-Based Visualization: Theory, Method and Applications: Yunxiao Chen, Zhiliang Ying, Haoran Zhang, 2021.
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Mixing Time of Metropolis-Hastings for Bayesian Community Detection: Bumeng Zhuo, Chao Gao, 2021.
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Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm: Defeng Sun, Kim-Chuan Toh, Yancheng Yuan, 2021.
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A Unified Framework for Random Forest Prediction Error Estimation: Benjamin Lu, Johanna Hardin, 2021.
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Preference-based Online Learning with Dueling Bandits: A Survey: Viktor Bengs, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, Eyke Hüllermeier, 2021.
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Consistent estimation of small masses in feature sampling: Fadhel Ayed, Marco Battiston, Federico Camerlenghi, Stefano Favaro, 2021.
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The Decoupled Extended Kalman Filter for Dynamic Exponential-Family Factorization Models: Carlos A. Gomez-Uribe, Brian Karrer, 2021.
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An Empirical Study of Bayesian Optimization: Acquisition Versus Partition: Erich Merrill, Alan Fern, Xiaoli Fern, Nima Dolatnia, 2021.
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Regulating Greed Over Time in Multi-Armed Bandits: Stefano Tracà, Cynthia Rudin, Weiyu Yan, 2021.
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Domain Generalization by Marginal Transfer Learning: Gilles Blanchard, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, Clayton Scott, 2021.
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On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests: Krishnakumar Balasubramanian, Tong Li, Ming Yuan, 2021.
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Full list