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

Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements: Tian Tong, Cong Ma, Ashley Prater-Bennette, Erin Tripp, Yuejie Chi, 2022.
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Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods: Dachao Lin, Haishan Ye, Zhihua Zhang, 2022.
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Topologically penalized regression on manifolds: Olympio Hacquard, Krishnakumar Balasubramanian, Gilles Blanchard, Clément Levrard, Wolfgang Polonik, 2022.
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Fairness-Aware PAC Learning from Corrupted Data: Nikola Konstantinov, Christoph H. Lampert, 2022.
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Structure Learning for Directed Trees: Martin E. Jakobsen, Rajen D. Shah, Peter Bühlmann, Jonas Peters, 2022.
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ktrain: A Low-Code Library for Augmented Machine Learning: Arun S. Maiya, 2022. (Machine Learning Open Source Software Paper)
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A universally consistent learning rule with a universally monotone error: Vladimir Pestov, 2022.
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Statistical Rates of Convergence for Functional Partially Linear Support Vector Machines for Classification: Yingying Zhang, Yan-Yong Zhao, Heng Lian, 2022.
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Principal Components Bias in Over-parameterized Linear Models, and its Manifestation in Deep Neural Networks: Guy Hacohen, Daphna Weinshall, 2022.
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Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach: Yanwei Jia, Xun Yu Zhou, 2022.
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Truncated Emphatic Temporal Difference Methods for Prediction and Control: Shangtong Zhang, Shimon Whiteson, 2022.
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Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning: Sébastien Forestier, Rémy Portelas, Yoan Mollard, Pierre-Yves Oudeyer, 2022.
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Universal Approximation of Functions on Sets: Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke, Michael A. Osborne, Ingmar Posner, 2022.
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EV-GAN: Simulation of extreme events with ReLU neural networks: Michaël Allouche, Stéphane Girard, Emmanuel Gobet, 2022.
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Implicit Differentiation for Fast Hyperparameter Selection in Non-Smooth Convex Learning: Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon, 2022.
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Online Nonnegative CP-dictionary Learning for Markovian Data: Hanbaek Lyu, Christopher Strohmeier, Deanna Needell, 2022.
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On the Robustness to Misspecification of α-posteriors and Their Variational Approximations: Marco Avella Medina, José Luis Montiel Olea, Cynthia Rush, Amilcar Velez, 2022.
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Adversarial Robustness Guarantees for Gaussian Processes: Andrea Patane, Arno Blaas, Luca Laurenti, Luca Cardelli, Stephen Roberts, Marta Kwiatkowska, 2022.
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A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning: Andrew Patterson, Adam White, Martha White, 2022.
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A Momentumized, Adaptive, Dual Averaged Gradient Method: Aaron Defazio, Samy Jelassi, 2022.
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Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning: Alex A. Gorodetsky, Cosmin Safta, John D. Jakeman, 2022.
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A Perturbation-Based Kernel Approximation Framework: Roy Mitz, Yoel Shkolnisky, 2022.
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The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks: Konstantinos Pantazis, Avanti Athreya, Jesus Arroyo, William N Frost, Evan S Hill, Vince Lyzinski, 2022.
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Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems: Yahya Sattar, Samet Oymak, 2022.
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No Weighted-Regret Learning in Adversarial Bandits with Delays: Ilai Bistritz, Zhengyuan Zhou, Xi Chen, Nicholas Bambos, Jose Blanchet, 2022.
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Exact simulation of diffusion first exit times: algorithm acceleration: Samuel Herrmann, Cristina Zucca, 2022.
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On the Efficiency of Entropic Regularized Algorithms for Optimal Transport: Tianyi Lin, Nhat Ho, Michael I. Jordan, 2022.
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Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization: Quanming Yao, Yaqing Wang, Bo Han, James T. Kwok, 2022.
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Recovery and Generalization in Over-Realized Dictionary Learning: Jeremias Sulam, Chong You, Zhihui Zhu, 2022.
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Transfer Learning in Information Criteria-based Feature Selection: Shaohan Chen, Nikolaos V. Sahinidis, Chuanhou Gao, 2022.
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Manifold Coordinates with Physical Meaning: Samson J. Koelle, Hanyu Zhang, Marina Meila, Yu-Chia Chen, 2022.
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An Optimization-centric View on Bayes' Rule: Reviewing and Generalizing Variational Inference: Jeremias Knoblauch, Jack Jewson, Theodoros Damoulas, 2022.
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Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence: Julie Nutini, Issam Laradji, Mark Schmidt, 2022.
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Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks: Alexander Shevchenko, Vyacheslav Kungurtsev, Marco Mondelli, 2022.
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On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC): Washim Uddin Mondal, Mridul Agarwal, Vaneet Aggarwal, Satish V. Ukkusuri, 2022.
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Power Iteration for Tensor PCA: Jiaoyang Huang, Daniel Z. Huang, Qing Yang, Guang Cheng, 2022.
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Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations: Haoyuan Chen, Liang Ding, Rui Tuo, 2022.
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Neural Estimation of Statistical Divergences: Sreejith Sreekumar, Ziv Goldfeld, 2022.
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Foolish Crowds Support Benign Overfitting: Niladri S. Chatterji, Philip M. Long, 2022.
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Darts: User-Friendly Modern Machine Learning for Time Series: Julien Herzen, Francesco Lässig, Samuele Giuliano Piazzetta, Thomas Neuer, Léo Tafti, Guillaume Raille, Tomas Van Pottelbergh, Marek Pasieka, Andrzej Skrodzki, Nicolas Huguenin, Maxime Dumonal, Jan Kościsz, Dennis Bader, Frédérick Gusset, Mounir Benheddi, Camila Williamson, Michal Kosinski, Matej Petrik, Gaël Grosch, 2022. (Machine Learning Open Source Software Paper)
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Provable Tensor-Train Format Tensor Completion by Riemannian Optimization: Jian-Feng Cai, Jingyang Li, Dong Xia, 2022.
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Depth separation beyond radial functions: Luca Venturi, Samy Jelassi, Tristan Ozuch, Joan Bruna, 2022.
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Online Mirror Descent and Dual Averaging: Keeping Pace in the Dynamic Case: Huang Fang, Nicholas J. A. Harvey, Victor S. Portella, Michael P. Friedlander, 2022.
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Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity: William Fedus, Barret Zoph, Noam Shazeer, 2022.
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A spectral-based analysis of the separation between two-layer neural networks and linear methods: Lei Wu, Jihao Long, 2022.
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Under-bagging Nearest Neighbors for Imbalanced Classification: Hanyuan Hang, Yuchao Cai, Hanfang Yang, Zhouchen Lin, 2022.
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OVERT: An Algorithm for Safety Verification of Neural Network Control Policies for Nonlinear Systems: Chelsea Sidrane, Amir Maleki, Ahmed Irfan, Mykel J. Kochenderfer, 2022.
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An Error Analysis of Generative Adversarial Networks for Learning Distributions: Jian Huang, Yuling Jiao, Zhen Li, Shiao Liu, Yang Wang, Yunfei Yang, 2022.
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Cauchy–Schwarz Regularized Autoencoder: Linh Tran, Maja Pantic, Marc Peter Deisenroth, 2022.
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ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction: Kwan Ho Ryan Chan, Yaodong Yu, Chong You, Haozhi Qi, John Wright, Yi Ma, 2022.
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The Two-Sided Game of Googol: José Correa, Andrés Cristi, Boris Epstein, José Soto, 2022.
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Sum of Ranked Range Loss for Supervised Learning: Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu, 2022.
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Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces: Masaaki Imaizumi, Kenji Fukumizu, 2022.
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EiGLasso for Scalable Sparse Kronecker-Sum Inverse Covariance Estimation: Jun Ho Yoon, Seyoung Kim, 2022.
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Conditions and Assumptions for Constraint-based Causal Structure Learning: Kayvan Sadeghi, Terry Soo, 2022.
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Bayesian subset selection and variable importance for interpretable prediction and classification: Daniel R. Kowal, 2022.
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IALE: Imitating Active Learner Ensembles: Christoffer Löffler, Christopher Mutschler, 2022.
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Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold: Bokun Wang, Shiqian Ma, Lingzhou Xue, 2022.
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Globally Injective ReLU Networks: Michael Puthawala, Konik Kothari, Matti Lassas, Ivan Dokmanić, Maarten de Hoop, 2022.
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Efficient Least Squares for Estimating Total Effects under Linearity and Causal Sufficiency: F. Richard Guo, Emilija Perković, 2022.
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The EM Algorithm is Adaptively-Optimal for Unbalanced Symmetric Gaussian Mixtures: Nir Weinberger, Guy Bresler, 2022.
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Sufficient reductions in regression with mixed predictors: Efstathia Bura, Liliana Forzani, Rodrigo Garcia Arancibia, Pamela Llop, Diego Tomassi, 2022.
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Towards An Efficient Approach for the Nonconvex lp Ball Projection: Algorithm and Analysis: Xiangyu Yang, Jiashan Wang, Hao Wang, 2022.
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Total Stability of SVMs and Localized SVMs: Hannes Köhler, Andreas Christmann, 2022.
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Distributed Learning of Finite Gaussian Mixtures: Qiong Zhang, Jiahua Chen, 2022.
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PECOS: Prediction for Enormous and Correlated Output Spaces: Hsiang-Fu Yu, Kai Zhong, Jiong Zhang, Wei-Cheng Chang, Inderjit S. Dhillon, 2022.
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Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective: Daniel Sanz-Alonso, Ruiyi Yang, 2022.
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Rethinking Nonlinear Instrumental Variable Models through Prediction Validity: Chunxiao Li, Cynthia Rudin, Tyler H. McCormick, 2022.
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Attraction-Repulsion Spectrum in Neighbor Embeddings: Jan Niklas Böhm, Philipp Berens, Dmitry Kobak, 2022.
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Multiple Testing in Nonparametric Hidden Markov Models: An Empirical Bayes Approach: Kweku Abraham, Ismaël Castillo, Elisabeth Gassiat, 2022.
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Regularized K-means Through Hard-Thresholding: Jakob Raymaekers, Ruben H. Zamar, 2022.
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Gauss-Legendre Features for Gaussian Process Regression: Paz Fink Shustin, Haim Avron, 2022.
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When Hardness of Approximation Meets Hardness of Learning: Eran Malach, Shai Shalev-Shwartz, 2022.
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Accelerating Adaptive Cubic Regularization of Newton's Method via Random Sampling: Xi Chen, Bo Jiang, Tianyi Lin, Shuzhong Zhang, 2022.
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Machine Learning on Graphs: A Model and Comprehensive Taxonomy: Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, Kevin Murphy, 2022.
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Generalized Ambiguity Decomposition for Ranking Ensemble Learning: Hongzhi Liu, Yingpeng Du, Zhonghai Wu, 2022.
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CD-split and HPD-split: Efficient Conformal Regions in High Dimensions: Rafael Izbicki, Gilson Shimizu, Rafael B. Stern, 2022.
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Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing: Chao Shen, Yu-Ting Lin, Hau-Tieng Wu, 2022.
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A Distribution Free Conditional Independence Test with Applications to Causal Discovery: Zhanrui Cai, Runze Li, Yaowu Zhang, 2022.
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Distributed Bayesian Varying Coefficient Modeling Using a Gaussian Process Prior: Rajarshi Guhaniyogi, Cheng Li, Terrance D. Savitsky, Sanvesh Srivastava, 2022.
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Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping: Yichi Zhang, Molei Liu, Matey Neykov, Tianxi Cai, 2022.
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FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting: Boxin Zhao, Y. Samuel Wang, Mladen Kolar, 2022.
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Dependent randomized rounding for clustering and partition systems with knapsack constraints: David G. Harris, Thomas Pensyl, Aravind Srinivasan, Khoa Trinh, 2022.
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Posterior Asymptotics for Boosted Hierarchical Dirichlet Process Mixtures: Marta Catalano, Pierpaolo De Blasi, Antonio Lijoi, Igor Pruenster, 2022.
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Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors: Yuling Yao, Aki Vehtari, Andrew Gelman, 2022.
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Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism: Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos, 2022.
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Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits: Lilian Besson, Emilie Kaufmann, Odalric-Ambrym Maillard, Julien Seznec, 2022.
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Joint Inference of Multiple Graphs from Matrix Polynomials: Madeline Navarro, Yuhao Wang, Antonio G. Marques, Caroline Uhler, Santiago Segarra, 2022.
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Mutual Information Constraints for Monte-Carlo Objectives to Prevent Posterior Collapse Especially in Language Modelling: Gábor Melis, András György, Phil Blunsom, 2022.
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All You Need is a Good Functional Prior for Bayesian Deep Learning: Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Maurizio Filippone, 2022.
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A Kernel Two-Sample Test for Functional Data: George Wynne, Andrew B. Duncan, 2022.
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Batch Normalization Preconditioning for Neural Network Training: Susanna Lange, Kyle Helfrich, Qiang Ye, 2022.
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Multiple-Splitting Projection Test for High-Dimensional Mean Vectors: Wanjun Liu, Xiufan Yu, Runze Li, 2022.
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Generalized Sparse Additive Models: Asad Haris, Noah Simon, Ali Shojaie, 2022.
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Asymptotic Network Independence and Step-Size for a Distributed Subgradient Method: Alex Olshevsky, 2022.
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Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters: Wei Zhu, Qiang Qiu, Robert Calderbank, Guillermo Sapiro, Xiuyuan Cheng, 2022.
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Are All Layers Created Equal?: Chiyuan Zhang, Samy Bengio, Yoram Singer, 2022.
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New Insights for the Multivariate Square-Root Lasso: Aaron J. Molstad, 2022.
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On the Complexity of Approximating Multimarginal Optimal Transport: Tianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan, 2022.
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Stochastic Zeroth-Order Optimization under Nonstationarity and Nonconvexity: Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra, 2022.
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Additive nonlinear quantile regression in ultra-high dimension: Ben Sherwood, Adam Maidman, 2022.
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The AIM and EM Algorithms for Learning from Coarse Data: Manfred Jaeger, 2022.
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Sparse Additive Gaussian Process Regression: Hengrui Luo, Giovanni Nattino, Matthew T. Pratola, 2022.
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A Unifying Framework for Variance-Reduced Algorithms for Findings Zeroes of Monotone operators: Xun Zhang, William B. Haskell, Zhisheng Ye, 2022.
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Causal Classification: Treatment Effect Estimation vs. Outcome Prediction: Carlos Fernández-Loría, Foster Provost, 2022.
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A Statistical Approach for Optimal Topic Model Identification: Craig M. Lewis, Francesco Grossetti, 2022.
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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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Structure-adaptive Manifold Estimation: Nikita Puchkin, Vladimir Spokoiny, 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.
[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