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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.

Optimal Feedback Law Recovery by Gradient-Augmented Sparse Polynomial Regression
Behzad Azmi, Dante Kalise, Karl Kunisch, 2021.

Understanding Recurrent Neural Networks Using Nonequilibrium Response Theory
Soon Hoe Lim, 2021.

Optimal Structured Principal Subspace Estimation: Metric Entropy and Minimax Rates
Tony Cai, Hongzhe Li, Rong Ma, 2021.

RaSE: Random Subspace Ensemble Classification
Ye Tian, Yang Feng, 2021.
[abs][pdf][bib]      [code]

Wasserstein barycenters can be computed in polynomial time in fixed dimension
Jason M Altschuler, Enric Boix-Adsera, 2021.
[abs][pdf][bib]      [code]

Banach Space Representer Theorems for Neural Networks and Ridge Splines
Rahul Parhi, Robert D. Nowak, 2021.

High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm
Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan, 2021.

From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction
Henning Lange, Steven L. Brunton, J. Nathan Kutz, 2021.
[abs][pdf][bib]      [code]

Residual Energy-Based Models for Text
Anton Bakhtin, Yuntian Deng, Sam Gross, Myle Ott, Marc'Aurelio Ranzato, Arthur Szlam, 2021.

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)
[abs][pdf][bib]      [code]

Risk-Averse Learning by Temporal Difference Methods with Markov Risk Measures
Umit Köse, Andrzej Ruszczyński, 2021.

A Bayesian Contiguous Partitioning Method for Learning Clustered Latent Variables
Zhao Tang Luo, Huiyan Sang, Bani Mallick, 2021.

Multi-class Gaussian Process Classification with Noisy Inputs
Carlos Villacampa-Calvo, Bryan Zaldívar, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, 2021.
[abs][pdf][bib]      [code]

Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation
Melkior Ornik, Ufuk Topcu, 2021.

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.
[abs][pdf][bib]      [code]

Asynchronous Online Testing of Multiple Hypotheses
Tijana Zrnic, Aaditya Ramdas, Michael I. Jordan, 2021.

Learning interaction kernels in heterogeneous systems of agents from multiple trajectories
Fei Lu, Mauro Maggioni, Sui Tang, 2021.
[abs][pdf][bib]      [code]

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.

A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms
Oliver Kroemer, Scott Niekum, George Konidaris, 2021.

Single and Multiple Change-Point Detection with Differential Privacy
Wanrong Zhang, Sara Krehbiel, Rui Tuo, Yajun Mei, Rachel Cummings, 2021.

Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits
Julian Zimmert, Yevgeny Seldin, 2021.

Inference In High-dimensional Single-Index Models Under Symmetric Designs
Hamid Eftekhari, Moulinath Banerjee, Ya'acov Ritov, 2021.
[abs][pdf][bib]      [code]

Finite Time LTI System Identification
Tuhin Sarkar, Alexander Rakhlin, Munther A. Dahleh, 2021.

Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions
Yunwen Lei, Ting Hu, Ke Tang, 2021.

Entangled Kernels - Beyond Separability
Riikka Huusari, Hachem Kadri, 2021.
[abs][pdf][bib]      [code]

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.

When random initializations help: a study of variational inference for community detection
Purnamrita Sarkar, Y. X. Rachel Wang, Soumendu S. Mukherjee, 2021.

A Fast Globally Linearly Convergent Algorithm for the Computation of Wasserstein Barycenters
Lei Yang, Jia Li, Defeng Sun, Kim-Chuan Toh, 2021.

Aggregated Hold-Out
Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle, 2021.

Ranking and synchronization from pairwise measurements via SVD
Alexandre d'Aspremont, Mihai Cucuringu, Hemant Tyagi, 2021.

A Unified Sample Selection Framework for Output Noise Filtering: An Error-Bound Perspective
Gaoxia Jiang, Wenjian Wang, Yuhua Qian, Jiye Liang, 2021.

Continuous Time Analysis of Momentum Methods
Nikola B. Kovachki, Andrew M. Stuart, 2021.

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)
[abs][pdf][bib]      [code]

Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-examples
Jagdeep Singh Bhatia, 2021.

On Multi-Armed Bandit Designs for Dose-Finding Trials
Maryam Aziz, Emilie Kaufmann, Marie-Karelle Riviere, 2021.

Homogeneity Structure Learning in Large-scale Panel Data with Heavy-tailed Errors
Xiao Di, Yuan Ke, Runze Li, 2021.

Global and Quadratic Convergence of Newton Hard-Thresholding Pursuit
Shenglong Zhou, Naihua Xiu, Hou-Duo Qi, 2021.

Unfolding-Model-Based Visualization: Theory, Method and Applications
Yunxiao Chen, Zhiliang Ying, Haoran Zhang, 2021.
[abs][pdf][bib]      [code]

Mixing Time of Metropolis-Hastings for Bayesian Community Detection
Bumeng Zhuo, Chao Gao, 2021.

Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm
Defeng Sun, Kim-Chuan Toh, Yancheng Yuan, 2021.

A Unified Framework for Random Forest Prediction Error Estimation
Benjamin Lu, Johanna Hardin, 2021.

Preference-based Online Learning with Dueling Bandits: A Survey
Viktor Bengs, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, Eyke Hüllermeier, 2021.

Consistent estimation of small masses in feature sampling
Fadhel Ayed, Marco Battiston, Federico Camerlenghi, Stefano Favaro, 2021.

The Decoupled Extended Kalman Filter for Dynamic Exponential-Family Factorization Models
Carlos A. Gomez-Uribe, Brian Karrer, 2021.

An Empirical Study of Bayesian Optimization: Acquisition Versus Partition
Erich Merrill, Alan Fern, Xiaoli Fern, Nima Dolatnia, 2021.
[abs][pdf][bib]      [code]

Regulating Greed Over Time in Multi-Armed Bandits
Stefano Tracà, Cynthia Rudin, Weiyu Yan, 2021.
[abs][pdf][bib]      [code]

Domain Generalization by Marginal Transfer Learning
Gilles Blanchard, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, Clayton Scott, 2021.
[abs][pdf][bib]      [code]

On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests
Krishnakumar Balasubramanian, Tong Li, Ming Yuan, 2021.

Full list

© JMLR 2021.