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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- (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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