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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


Latest papers

Inherent Tradeoffs in Learning Fair Representations
Han Zhao, Geoffrey J. Gordon, 2022.
[abs][pdf][bib]

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)
[abs][pdf][bib]      [code]

Bayesian Pseudo Posterior Mechanism under Asymptotic Differential Privacy
Terrance D. Savitsky, Matthew R.Williams, Jingchen Hu, 2022.
[abs][pdf][bib]

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)
[abs][pdf][bib]      [code]

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)
[abs][pdf][bib]      [code]

LinCDE: Conditional Density Estimation via Lindsey's Method
Zijun Gao, Trevor Hastie, 2022.
[abs][pdf][bib]

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)
[abs][pdf][bib]      [code]

Analytically Tractable Hidden-States Inference in Bayesian Neural Networks
Luong-Ha Nguyen, James-A. Goulet, 2022.
[abs][pdf][bib]

Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection
Xinyi Wang, Lang Tong, 2022.
[abs][pdf][bib]

Overparameterization of Deep ResNet: Zero Loss and Mean-field Analysis
Zhiyan Ding, Shi Chen, Qin Li, Stephen J. Wright, 2022.
[abs][pdf][bib]

Cascaded Diffusion Models for High Fidelity Image Generation
Jonathan Ho, Chitwan Saharia, William Chan, David J. Fleet, Mohammad Norouzi, Tim Salimans, 2022.
[abs][pdf][bib]      [code]

Beyond Sub-Gaussian Noises: Sharp Concentration Analysis for Stochastic Gradient Descent
Wanrong Zhu, Zhipeng Lou, Wei Biao Wu, 2022.
[abs][pdf][bib]

Optimal Transport for Stationary Markov Chains via Policy Iteration
Kevin O'Connor, Kevin McGoff, Andrew B. Nobel, 2022.
[abs][pdf][bib]      [code]

PAC Guarantees and Effective Algorithms for Detecting Novel Categories
Si Liu, Risheek Garrepalli, Dan Hendrycks, Alan Fern, Debashis Mondal, Thomas G. Dietterich, 2022.
[abs][pdf][bib]      [code]

Sampling Permutations for Shapley Value Estimation
Rory Mitchell, Joshua Cooper, Eibe Frank, Geoffrey Holmes, 2022.
[abs][pdf][bib]

Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks
Zhong Li, Jiequn Han, Weinan E, Qianxiao Li, 2022.
[abs][pdf][bib]

The correlation-assisted missing data estimator
Timothy I. Cannings, Yingying Fan, 2022.
[abs][pdf][bib]

Structure-adaptive Manifold Estimation
Nikita Puchkin, Vladimir Spokoiny, 2022.
[abs][pdf][bib]

(f,Gamma)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics
Jeremiah Birrell, Paul Dupuis, Markos A. Katsoulakis, Yannis Pantazis, Luc Rey-Bellet, 2022.
[abs][pdf][bib]

Score Matched Neural Exponential Families for Likelihood-Free Inference
Lorenzo Pacchiardi, Ritabrata Dutta, 2022.
[abs][pdf][bib]      [code]

Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric
Matteo Pegoraro, Mario Beraha, 2022.
[abs][pdf][bib]      [code]

Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization
Feihu Huang, Shangqian Gao, Jian Pei, Heng Huang, 2022.
[abs][pdf][bib]

Optimality and Stability in Non-Convex Smooth Games
Guojun Zhang, Pascal Poupart, Yaoliang Yu, 2022.
[abs][pdf][bib]

SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks
Weijing Tang, Jiaqi Ma, Qiaozhu Mei, Ji Zhu, 2022.
[abs][pdf][bib]      [code]

Model Averaging Is Asymptotically Better Than Model Selection For Prediction
Tri M. Le, Bertrand S. Clarke, 2022.
[abs][pdf][bib]

Active Learning for Nonlinear System Identification with Guarantees
Horia Mania, Michael I. Jordan, Benjamin Recht, 2022.
[abs][pdf][bib]

An improper estimator with optimal excess risk in misspecified density estimation and logistic regression
Jaouad Mourtada, Stéphane Gaïffas, 2022.
[abs][pdf][bib]

A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One
Augusto Fasano, Daniele Durante, 2022.
[abs][pdf][bib]

Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning
Kaiyi Ji, Junjie Yang, Yingbin Liang, 2022.
[abs][pdf][bib]

Novel Min-Max Reformulations of Linear Inverse Problems
Mohammed Rayyan Sheriff, Debasish Chatterjee, 2022.
[abs][pdf][bib]

Data-Derived Weak Universal Consistency
Narayana Santhanam, Venkatachalam Anantharam, Wojciech Szpankowski, 2022.
[abs][pdf][bib]

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.
[abs][pdf][bib]      [code]

Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting
Maxime Vono, Daniel Paulin, Arnaud Doucet, 2022.
[abs][pdf][bib]

On Biased Stochastic Gradient Estimation
Derek Driggs, Jingwei Liang, Carola-Bibiane Schönlieb, 2022.
[abs][pdf][bib]

Fast and Robust Rank Aggregation against Model Misspecification
Yuangang Pan, Ivor W. Tsang, Weijie Chen, Gang Niu, Masashi Sugiyama, 2022.
[abs][pdf][bib]

LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data
Ali Eshragh, Fred Roosta, Asef Nazari, Michael W. Mahoney, 2022.
[abs][pdf][bib]

Evolutionary Variational Optimization of Generative Models
Jakob Drefs, Enrico Guiraud, Jörg Lücke, 2022.
[abs][pdf][bib]      [code]

Supervised Dimensionality Reduction and Visualization using Centroid-Encoder
Tomojit Ghosh, Michael Kirby, 2022.
[abs][pdf][bib]      [code]

Universal Approximation in Dropout Neural Networks
Oxana A. Manita, Mark A. Peletier, Jacobus W. Portegies, Jaron Sanders, Albert Senen-Cerda, 2022.
[abs][pdf][bib]

Decimated Framelet System on Graphs and Fast G-Framelet Transforms
Xuebin Zheng, Bingxin Zhou, Yu Guang Wang, Xiaosheng Zhuang, 2022.
[abs][pdf][bib]      [code]

Spatial Multivariate Trees for Big Data Bayesian Regression
Michele Peruzzi, David B. Dunson, 2022.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

A Stochastic Bundle Method for Interpolation
Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar, 2022.
[abs][pdf][bib]      [code]

On Generalizations of Some Distance Based Classifiers for HDLSS Data
Sarbojit Roy, Soham Sarkar, Subhajit Dutta, Anil K. Ghosh, 2022.
[abs][pdf][bib]

Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality
Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet, 2022.
[abs][pdf][bib]      [code]

Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems
Jayakumar Subramanian, Amit Sinha, Raihan Seraj, Aditya Mahajan, 2022.
[abs][pdf][bib]      [code]

Near Optimality of Finite Memory Feedback Policies in Partially Observed Markov Decision Processes
Ali Kara, Serdar Yuksel, 2022.
[abs][pdf][bib]

Interpolating Predictors in High-Dimensional Factor Regression
Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp, 2022.
[abs][pdf][bib]

Scaling Laws from the Data Manifold Dimension
Utkarsh Sharma, Jared Kaplan, 2022.
[abs][pdf][bib]      [code]

Deep Learning in Target Space
Michael Fairbank, Spyridon Samothrakis, Luca Citi, 2022.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

XAI Beyond Classification: Interpretable Neural Clustering
Xi Peng, Yunfan Li, Ivor W. Tsang, Hongyuan Zhu, Jiancheng Lv, Joey Tianyi Zhou, 2022.
[abs][pdf][bib]      [code]

Empirical Risk Minimization under Random Censorship
Guillaume Ausset, Stephan Clémençon, François Portier, 2022.
[abs][pdf][bib]

Exploiting locality in high-dimensional Factorial hidden Markov models
Lorenzo Rimella, Nick Whiteley, 2022.
[abs][pdf][bib]      [code]

Recovering shared structure from multiple networks with unknown edge distributions
Keith Levin, Asad Lodhia, Elizaveta Levina, 2022.
[abs][pdf][bib]

Debiased Distributed Learning for Sparse Partial Linear Models in High Dimensions
Shaogao Lv, Heng Lian, 2022.
[abs][pdf][bib]

Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models
Subhabrata Majumdar, George Michailidis, 2022.
[abs][pdf][bib]      [code]

Full list

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