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
- Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference
- Jiyuan Tu, Weidong Liu, Xiaojun Mao, Xi Chen, 2021.
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- PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
- Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Sahand Sharifzadeh, Volker Tresp, Jens Lehmann, 2021.
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- Knowing what You Know: valid and validated confidence sets in multiclass and multilabel prediction
- Maxime Cauchois, Suyash Gupta, John C. Duchi, 2021.
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- Communication-Efficient Distributed Covariance Sketch, with Application to Distributed PCA
- Zengfeng Huang, Xuemin Lin, Wenjie Zhang, Ying Zhang, 2021.
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- Is SGD a Bayesian sampler? Well, almost
- Chris Mingard, Guillermo Valle-Pérez, Joar Skalse, Ard A. Louis, 2021.
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- POT: Python Optimal Transport
- Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, Titouan Vayer, 2021. (Machine Learning Open Source Software Paper)
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- ChainerRL: A Deep Reinforcement Learning Library
- Yasuhiro Fujita, Prabhat Nagarajan, Toshiki Kataoka, Takahiro Ishikawa, 2021. (Machine Learning Open Source Software Paper)
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- Analyzing the discrepancy principle for kernelized spectral filter learning algorithms
- Alain Celisse, Martin Wahl, 2021.
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- Kernel Operations on the GPU, with Autodiff, without Memory Overflows
- Benjamin Charlier, Jean Feydy, Joan Alexis Glaunès, François-David Collin, Ghislain Durif, 2021. (Machine Learning Open Source Software Paper)
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- Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives
- Michael Muehlebach, Michael I. Jordan, 2021.
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- Prediction against a limited adversary
- Erhan Bayraktar, Ibrahim Ekren, Xin Zhang, 2021.
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- Phase Diagram for Two-layer ReLU Neural Networks at Infinite-width Limit
- Tao Luo, Zhi-Qin John Xu, Zheng Ma, Yaoyu Zhang, 2021.
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- Testing Conditional Independence via Quantile Regression Based Partial Copulas
- Lasse Petersen, Niels Richard Hansen, 2021.
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- Determining the Number of Communities in Degree-corrected Stochastic Block Models
- Shujie Ma, Liangjun Su, Yichong Zhang, 2021.
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- Path Length Bounds for Gradient Descent and Flow
- Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas, 2021.
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- A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family
- Trambak Banerjee, Qiang Liu, Gourab Mukherjee, Wengunag Sun, 2021.
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- Dynamic Tensor Recommender Systems
- Yanqing Zhang, Xuan Bi, Niansheng Tang, Annie Qu, 2021.
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- Sparse Tensor Additive Regression
- Botao Hao, Boxiang Wang, Pengyuan Wang, Jingfei Zhang, Jian Yang, Will Wei Sun, 2021.
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- Geometric structure of graph Laplacian embeddings
- Nicolás García Trillos, Franca Hoffmann, Bamdad Hosseini, 2021.
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- How to Gain on Power: Novel Conditional Independence Tests Based on Short Expansion of Conditional Mutual Information
- Mariusz Kubkowski, Jan Mielniczuk, Paweł Teisseyre, 2021.
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- A Distributed Method for Fitting Laplacian Regularized Stratified Models
- Jonathan Tuck, Shane Barratt, Stephen Boyd, 2021.
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- Predictive Learning on Hidden Tree-Structured Ising Models
- Konstantinos E. Nikolakakis, Dionysios S. Kalogerias, Anand D. Sarwate, 2021.
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- Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing Approach
- Zhe Fei, Yi Li, 2021.
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- Normalizing Flows for Probabilistic Modeling and Inference
- George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan, 2021.
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- Incorporating Unlabeled Data into Distributionally Robust Learning
- Charlie Frogner, Sebastian Claici, Edward Chien, Justin Solomon, 2021.
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- Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data
- Minjie Wang, Genevera I. Allen, 2021.
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- GemBag: Group Estimation of Multiple Bayesian Graphical Models
- Xinming Yang, Lingrui Gan, Naveen N. Narisetty, Feng Liang, 2021.
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- Subspace Clustering through Sub-Clusters
- Weiwei Li, Jan Hannig, Sayan Mukherjee, 2021.
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- Sparse and Smooth Signal Estimation: Convexification of L0-Formulations
- Alper Atamturk, Andres Gomez, Shaoning Han, 2021.
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- Projection-free Decentralized Online Learning for Submodular Maximization over Time-Varying Networks
- Junlong Zhu, Qingtao Wu, Mingchuan Zhang, Ruijuan Zheng, Keqin Li, 2021.
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- Structure Learning of Undirected Graphical Models for Count Data
- Nguyen Thi Kim Hue, Monica Chiogna, 2021.
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- 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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- 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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- 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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- 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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