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.

News

• 2023.01.20: Volume 23 completed; Volume 24 began.
• 2022.07.20: New special issue on climate change.
• 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

• Fairlearn: Assessing and Improving Fairness of AI Systems

  Hilde Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, Michael Madaio, 2023

• Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks

  Khurram Javed, Haseeb Shah, Richard S. Sutton, Martha White, 2023

• Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architectures

  Mike Heddes, Igor Nunes, Pere Vergés, Denis Kleyko, Danny Abraham, Tony Givargis, Alexandru Nicolau, Alexander Veidenbaum, 2023

• skrl: Modular and Flexible Library for Reinforcement Learning

  Antonio Serrano-Muñoz, Dimitrios Chrysostomou, Simon Bøgh, Nestor Arana-Arexolaleiba, 2023

• Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model

  Alexandra Sasha Luccioni, Sylvain Viguier, Anne-Laure Ligozat, 2023

• Adaptive False Discovery Rate Control with Privacy Guarantee

  Xintao Xia, Zhanrui Cai, 2023

• Atlas: Few-shot Learning with Retrieval Augmented Language Models

  Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, Edouard Grave, 2023

• Convex Reinforcement Learning in Finite Trials

  Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli, 2023

• Unbiased Multilevel Monte Carlo Methods for Intractable Distributions: MLMC Meets MCMC

  Tianze Wang, Guanyang Wang, 2023

• Improving multiple-try Metropolis with local balancing

  Philippe Gagnon, Florian Maire, Giacomo Zanella, 2023

• Importance Sparsification for Sinkhorn Algorithm

  Mengyu Li, Jun Yu, Tao Li, Cheng Meng, 2023

• Graph Attention Retrospective

  Kimon Fountoulakis, Amit Levi, Shenghao Yang, Aseem Baranwal, Aukosh Jagannath, 2023

• Confidence Intervals and Hypothesis Testing for High-dimensional Quantile Regression: Convolution Smoothing and Debiasing

  Yibo Yan, Xiaozhou Wang, Riquan Zhang, 2023

• Selection by Prediction with Conformal p-values

  Ying Jin, Emmanuel J. Candes, 2023

• Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics

  Kamélia Daudel, Joe Benton, Yuyang Shi, Arnaud Doucet, 2023

• Sparse Graph Learning from Spatiotemporal Time Series

  Andrea Cini, Daniele Zambon, Cesare Alippi, 2023

• Improved Powered Stochastic Optimization Algorithms for Large-Scale Machine Learning

  Zhuang Yang, 2023

• PaLM: Scaling Language Modeling with Pathways

  Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, Noah Fiedel, 2023

• Leaky Hockey Stick Loss: The First Negatively Divergent Margin-based Loss Function for Classification

  Oh-Ran Kwon, Hui Zou, 2023

• Efficient Computation of Rankings from Pairwise Comparisons

  M. E. J. Newman, 2023

• Scalable Computation of Causal Bounds

  Madhumitha Shridharan, Garud Iyengar, 2023

• Neural Q-learning for solving PDEs

  Samuel N. Cohen, Deqing Jiang, Justin Sirignano, 2023

• Tractable and Near-Optimal Adversarial Algorithms for Robust Estimation in Contaminated Gaussian Models

  Ziyue Wang, Zhiqiang Tan, 2023

• MultiZoo and MultiBench: A Standardized Toolkit for Multimodal Deep Learning

  Paul Pu Liang, Yiwei Lyu, Xiang Fan, Arav Agarwal, Yun Cheng, Louis-Philippe Morency, Ruslan Salakhutdinov, 2023

• Strategic Knowledge Transfer

  Max Olan Smith, Thomas Anthony, Michael P. Wellman, 2023

• Lifted Bregman Training of Neural Networks

  Xiaoyu Wang, Martin Benning, 2023

• Statistical Comparisons of Classifiers by Generalized Stochastic Dominance

  Christoph Jansen, Malte Nalenz, Georg Schollmeyer, Thomas Augustin, 2023

• Sample Complexity for Distributionally Robust Learning under chi-square divergence

  Zhengyu Zhou, Weiwei Liu, 2023

• Interpretable and Fair Boolean Rule Sets via Column Generation

  Connor Lawless, Sanjeeb Dash, Oktay Gunluk, Dennis Wei, 2023

• On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure

  Yunhua Xiang, Tianyu Zhang, Xu Wang, Ali Shojaie, Noah Simon, 2023

• Autoregressive Networks

  Binyan Jiang, Jialiang Li, Qiwei Yao, 2023

• Merlion: End-to-End Machine Learning for Time Series

  Aadyot Bhatnagar, Paul Kassianik, Chenghao Liu, Tian Lan, Wenzhuo Yang, Rowan Cassius, Doyen Sahoo, Devansh Arpit, Sri Subramanian, Gerald Woo, Amrita Saha, Arun Kumar Jagota, Gokulakrishnan Gopalakrishnan, Manpreet Singh, K C Krithika, Sukumar Maddineni, Daeki Cho, Bo Zong, Yingbo Zhou, Caiming Xiong, Silvio Savarese, Steven Hoi, Huan Wang, 2023

• Limits of Dense Simplicial Complexes

  T. Mitchell Roddenberry, Santiago Segarra, 2023

• RankSEG: A Consistent Ranking-based Framework for Segmentation

  Ben Dai, Chunlin Li, 2023

• Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data

  Bingqing Hu, Bin Nan, 2023

• Single Timescale Actor-Critic Method to Solve the Linear Quadratic Regulator with Convergence Guarantees

  Mo Zhou, Jianfeng Lu, 2023

• Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices

  Doudou Zhou, Tianxi Cai, Junwei Lu, 2023

• A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning

  Wei-Fang Sun, Cheng-Kuang Lee, Simon See, Chun-Yi Lee, 2023

• Functional L-Optimality Subsampling for Functional Generalized Linear Models with Massive Data

  Hua Liu, Jinhong You, Jiguo Cao, 2023

• Adaptation Augmented Model-based Policy Optimization

  Jian Shen, Hang Lai, Minghuan Liu, Han Zhao, Yong Yu, Weinan Zhang, 2023

• GANs as Gradient Flows that Converge

  Yu-Jui Huang, Yuchong Zhang, 2023

• Random Forests for Change Point Detection

  Malte Londschien, Peter Bühlmann, Solt Kovács, 2023

• Least Squares Model Averaging for Distributed Data

  Haili Zhang, Zhaobo Liu, Guohua Zou, 2023

• An Empirical Investigation of the Role of Pre-training in Lifelong Learning

  Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar, Emma Strubell, 2023

• Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications

  Marcel Wienöbst, Max Bannach, Maciej Liśkiewicz, 2023

• An Inexact Augmented Lagrangian Algorithm for Training Leaky ReLU Neural Network with Group Sparsity

  Wei Liu, Xin Liu, Xiaojun Chen, 2023

• Entropic Fictitious Play for Mean Field Optimization Problem

  Fan Chen, Zhenjie Ren, Songbo Wang, 2023

• GFlowNet Foundations

  Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J. Hu, Mo Tiwari, Emmanuel Bengio, 2023

• LibMTL: A Python Library for Deep Multi-Task Learning

  Baijiong Lin, Yu Zhang, 2023

• Minimax Risk Classifiers with 0-1 Loss

  Santiago Mazuelas, Mauricio Romero, Peter Grunwald, 2023

• Augmented Sparsifiers for Generalized Hypergraph Cuts

  Nate Veldt, Austin R. Benson, Jon Kleinberg, 2023

• Non-stationary Online Learning with Memory and Non-stochastic Control

  Peng Zhao, Yu-Hu Yan, Yu-Xiang Wang, Zhi-Hua Zhou, 2023

• L0Learn: A Scalable Package for Sparse Learning using L0 Regularization

  Hussein Hazimeh, Rahul Mazumder, Tim Nonet, 2023

• Buffered Asynchronous SGD for Byzantine Learning

  Yi-Rui Yang, Wu-Jun Li, 2023

• A Non-parametric View of FedAvg and FedProx:Beyond Stationary Points

  Lili Su, Jiaming Xu, Pengkun Yang, 2023

• Multiplayer Performative Prediction: Learning in Decision-Dependent Games

  Adhyyan Narang, Evan Faulkner, Dmitriy Drusvyatskiy, Maryam Fazel, Lillian J. Ratliff, 2023

• Variational Inverting Network for Statistical Inverse Problems of Partial Differential Equations

  Junxiong Jia, Yanni Wu, Peijun Li, Deyu Meng, 2023

• Model-based Causal Discovery for Zero-Inflated Count Data

  Junsouk Choi, Yang Ni, 2023

• Q-Learning for MDPs with General Spaces: Convergence and Near Optimality via Quantization under Weak Continuity

  Ali Kara, Naci Saldi, Serdar Yüksel, 2023

• CodaLab Competitions: An Open Source Platform to Organize Scientific Challenges

  Adrien Pavao, Isabelle Guyon, Anne-Catherine Letournel, Dinh-Tuan Tran, Xavier Baro, Hugo Jair Escalante, Sergio Escalera, Tyler Thomas, Zhen Xu, 2023

• Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency

  Tetiana Gorbach, Xavier de Luna, Juha Karvanen, Ingeborg Waernbaum, 2023

• Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data

  Vaidotas Simkus, Benjamin Rhodes, Michael U. Gutmann, 2023

• Clustering and Structural Robustness in Causal Diagrams

  Santtu Tikka, Jouni Helske, Juha Karvanen, 2023

• MMD Aggregated Two-Sample Test

  Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj, Arthur Gretton, 2023

• Divide-and-Conquer Fusion

  Ryan S.Y. Chan, Murray Pollock, Adam M. Johansen, Gareth O. Roberts, 2023

• PAC-learning for Strategic Classification

  Ravi Sundaram, Anil Vullikanti, Haifeng Xu, Fan Yao, 2023

• Insights into Ordinal Embedding Algorithms: A Systematic Evaluation

  Leena Chennuru Vankadara, Michael Lohaus, Siavash Haghiri, Faiz Ul Wahab, Ulrike von Luxburg, 2023

• Clustering with Tangles: Algorithmic Framework and Theoretical Guarantees

  Solveig Klepper, Christian Elbracht, Diego Fioravanti, Jakob Kneip, Luca Rendsburg, Maximilian Teegen, Ulrike von Luxburg, 2023

• Random Feature Neural Networks Learn Black-Scholes Type PDEs Without Curse of Dimensionality

  Lukas Gonon, 2023

• The Proximal ID Algorithm

  Ilya Shpitser, Zach Wood-Doughty, Eric J. Tchetgen Tchetgen, 2023

• Quantifying Network Similarity using Graph Cumulants

  Gecia Bravo-Hermsdorff, Lee M. Gunderson, Pierre-André Maugis, Carey E. Priebe, 2023

• Learning an Explicit Hyper-parameter Prediction Function Conditioned on Tasks

  Jun Shu, Deyu Meng, Zongben Xu, 2023

• On the Theoretical Equivalence of Several Trade-Off Curves Assessing Statistical Proximity

  Rodrigue Siry, Ryan Webster, Loic Simon, Julien Rabin, 2023

• Metrizing Weak Convergence with Maximum Mean Discrepancies

  Carl-Johann Simon-Gabriel, Alessandro Barp, Bernhard Schölkopf, Lester Mackey, 2023

• Quasi-Equivalence between Width and Depth of Neural Networks

  Fenglei Fan, Rongjie Lai, Ge Wang, 2023

• Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding

  Justin Grimmer, Dean Knox, Brandon Stewart, 2023

• Factor Graph Neural Networks

  Zhen Zhang, Mohammed Haroon Dupty, Fan Wu, Javen Qinfeng Shi, Wee Sun Lee, 2023

• Dropout Training is Distributionally Robust Optimal

  José Blanchet, Yang Kang, José Luis Montiel Olea, Viet Anh Nguyen, Xuhui Zhang, 2023

• Variational Inference for Deblending Crowded Starfields

  Runjing Liu, Jon D. McAuliffe, Jeffrey Regier, The LSST Dark Energy Science Collaboration, 2023

• F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning

  Wenhao Li, Bo Jin, Xiangfeng Wang, Junchi Yan, Hongyuan Zha, 2023

• Comprehensive Algorithm Portfolio Evaluation using Item Response Theory

  Sevvandi Kandanaarachchi, Kate Smith-Miles, 2023

• Evaluating Instrument Validity using the Principle of Independent Mechanisms

  Patrick F. Burauel, 2023

• Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity

  Kaiqing Zhang, Sham M. Kakade, Tamer Basar, Lin F. Yang, 2023

• Posterior Consistency for Bayesian Relevance Vector Machines

  Xiao Fang, Malay Ghosh, 2023

• From Classification Accuracy to Proper Scoring Rules: Elicitability of Probabilistic Top List Predictions

  Johannes Resin, 2023

• Beyond the Golden Ratio for Variational Inequality Algorithms

  Ahmet Alacaoglu, Axel Böhm, Yura Malitsky, 2023

• Incremental Learning in Diagonal Linear Networks

  Raphaël Berthier, 2023

• Small Transformers Compute Universal Metric Embeddings

  Anastasis Kratsios, Valentin Debarnot, Ivan Dokmanić, 2023

• DART: Distance Assisted Recursive Testing

  Xuechan Li, Anthony D. Sung, Jichun Xie, 2023

• Inference on the Change Point under a High Dimensional Covariance Shift

  Abhishek Kaul, Hongjin Zhang, Konstantinos Tsampourakis, George Michailidis, 2023

• Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start

  Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo, 2023

• A Parameter-Free Conditional Gradient Method for Composite Minimization under Hölder Condition

  Masaru Ito, Zhaosong Lu, Chuan He, 2023

• Robust Methods for High-Dimensional Linear Learning

  Ibrahim Merad, Stéphane Gaïffas, 2023

• A Framework and Benchmark for Deep Batch Active Learning for Regression

  David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart, 2023

• Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification

  Gavin Zhang, Salar Fattahi, Richard Y. Zhang, 2023

• Flexible Model Aggregation for Quantile Regression

  Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani, 2023

• q-Learning in Continuous Time

  Yanwei Jia, Xun Yu Zhou, 2023

• Multivariate Soft Rank via Entropy-Regularized Optimal Transport: Sample Efficiency and Generative Modeling

  Shoaib Bin Masud, Matthew Werenski, James M. Murphy, Shuchin Aeron, 2023

• Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations

  Arnab Ganguly, Riten Mitra, Jinpu Zhou, 2023

• Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees

  Hamid Reza Feyzmahdavian, Mikael Johansson, 2023

• Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the in the O(epsilon^(-7/4)) Complexity

  Huan Li, Zhouchen Lin, 2023

• Integrating Random Effects in Deep Neural Networks

  Giora Simchoni, Saharon Rosset, 2023

• Adaptive Data Depth via Multi-Armed Bandits

  Tavor Baharav, Tze Leung Lai, 2023

• Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees

  Jonathan Brophy, Zayd Hammoudeh, Daniel Lowd, 2023

• Consistent Model-based Clustering using the Quasi-Bernoulli Stick-breaking Process

  Cheng Zeng, Jeffrey W Miller, Leo L Duan, 2023

• Selective inference for k-means clustering

  Yiqun T. Chen, Daniela M. Witten, 2023

• Generalization error bounds for multiclass sparse linear classifiers

  Tomer Levy, Felix Abramovich, 2023

• MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning

  Ming Zhou, Ziyu Wan, Hanjing Wang, Muning Wen, Runzhe Wu, Ying Wen, Yaodong Yang, Yong Yu, Jun Wang, Weinan Zhang, 2023

• Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning

  Titouan Vayer, Rémi Gribonval, 2023

• Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition

  Zhishuai Guo, Yan Yan, Zhuoning Yuan, Tianbao Yang, 2023

• Stochastic Optimization under Distributional Drift

  Joshua Cutler, Dmitriy Drusvyatskiy, Zaid Harchaoui, 2023

• Off-Policy Actor-Critic with Emphatic Weightings

  Eric Graves, Ehsan Imani, Raksha Kumaraswamy, Martha White, 2023

• Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning

  Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang, 2023

• Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering

  Noirrit Kiran Chandra, Antonio Canale, David B. Dunson, 2023

• Large sample spectral analysis of graph-based multi-manifold clustering

  Nicolas Garcia Trillos, Pengfei He, Chenghui Li, 2023

• On Tilted Losses in Machine Learning: Theory and Applications

  Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith, 2023

• Optimal Convergence Rates for Distributed Nystroem Approximation

  Jian Li, Yong Liu, Weiping Wang, 2023

• Jump Interval-Learning for Individualized Decision Making with Continuous Treatments

  Hengrui Cai, Chengchun Shi, Rui Song, Wenbin Lu, 2023

• Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games

  Ben Hambly, Renyuan Xu, Huining Yang, 2023

• Asymptotics of Network Embeddings Learned via Subsampling

  Andrew Davison, Morgane Austern, 2023

• Implicit Bias of Gradient Descent for Mean Squared Error Regression with Two-Layer Wide Neural Networks

  Hui Jin, Guido Montufar, 2023

• Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection

  Wenhao Li, Ningyuan Chen, L. Jeff Hong, 2023

• Sparse GCA and Thresholded Gradient Descent

  Sheng Gao, Zongming Ma, 2023

• MARS: A Second-Order Reduction Algorithm for High-Dimensional Sparse Precision Matrices Estimation

  Qian Li, Binyan Jiang, Defeng Sun, 2023

• Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators

  Benjamin Jakubowski, Sriram Somanchi, Edward McFowland III, Daniel B. Neill, 2023

• Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data

  Di Wang, Lijie Hu, Huanyu Zhang, Marco Gaboardi, Jinhui Xu, 2023

• A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition

  Patricia Wollstadt, Sebastian Schmitt, Michael Wibral, 2023

• Combinatorial Optimization and Reasoning with Graph Neural Networks

  Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Veličković, 2023

• A First Look into the Carbon Footprint of Federated Learning

  Xinchi Qiu, Titouan Parcollet, Javier Fernandez-Marques, Pedro P. B. Gusmao, Yan Gao, Daniel J. Beutel, Taner Topal, Akhil Mathur, Nicholas D. Lane, 2023

• An Eigenmodel for Dynamic Multilayer Networks

  Joshua Daniel Loyal, Yuguo Chen, 2023

• Graph Clustering with Graph Neural Networks

  Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller, 2023

• Euler-Lagrange Analysis of Generative Adversarial Networks

  Siddarth Asokan, Chandra Sekhar Seelamantula, 2023

• Statistical Robustness of Empirical Risks in Machine Learning

  Shaoyan Guo, Huifu Xu, Liwei Zhang, 2023

• HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation

  Weijie J. Su, Yuancheng Zhu, 2023

• Benign overfitting in ridge regression

  Alexander Tsigler, Peter L. Bartlett, 2023

• Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities

  Brian R. Bartoldson, Bhavya Kailkhura, Davis Blalock, 2023

• Minimal Width for Universal Property of Deep RNN

  Chang hoon Song, Geonho Hwang, Jun ho Lee, Myungjoo Kang, 2023

• Maximum likelihood estimation in Gaussian process regression is ill-posed

  Toni Karvonen, Chris J. Oates, 2023

• An Annotated Graph Model with Differential Degree Heterogeneity for Directed Networks

  Stefan Stein, Chenlei Leng, 2023

• A Unified Framework for Optimization-Based Graph Coarsening

  Manoj Kumar, Anurag Sharma, Sandeep Kumar, 2023

• Deep linear networks can benignly overfit when shallow ones do

  Niladri S. Chatterji, Philip M. Long, 2023

• SQLFlow: An Extensible Toolkit Integrating DB and AI

  Jun Zhou, Ke Zhang, Lin Wang, Hua Wu, Yi Wang, ChaoChao Chen, 2023

• Learning Good State and Action Representations for Markov Decision Process via Tensor Decomposition

  Chengzhuo Ni, Yaqi Duan, Munther Dahleh, Mengdi Wang, Anru R. Zhang, 2023

• Generalization Bounds for Adversarial Contrastive Learning

  Xin Zou, Weiwei Liu, 2023

• The Implicit Bias of Benign Overfitting

  Ohad Shamir, 2023

• The Hyperspherical Geometry of Community Detection: Modularity as a Distance

  Martijn Gösgens, Remco van der Hofstad, Nelly Litvak, 2023

• FLIP: A Utility Preserving Privacy Mechanism for Time Series

  Tucker McElroy, Anindya Roy, Gaurab Hore, 2023

• A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates

  Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi, 2023

• Dimensionless machine learning: Imposing exact units equivariance

  Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu, 2023

• Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priors

  Michail Spitieris, Ingelin Steinsland, 2023

• Risk Bounds for Positive-Unlabeled Learning Under the Selected At Random Assumption

  Olivier Coudray, Christine Keribin, Pascal Massart, Patrick Pamphile, 2023

• Concentration analysis of multivariate elliptic diffusions

  Lukas Trottner, Cathrine Aeckerle-Willems, Claudia Strauch, 2023

• Knowledge Hypergraph Embedding Meets Relational Algebra

  Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole, 2023

• Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics

  Mu Niu, Zhenwen Dai, Pokman Cheung, Yizhu Wang, 2023

• Sparse Training with Lipschitz Continuous Loss Functions and a Weighted Group L0-norm Constraint

  Michael R. Metel, 2023

• Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments

  Haixu Ma, Donglin Zeng, Yufeng Liu, 2023

• Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds

  Didong Li, Wenpin Tang, Sudipto Banerjee, 2023

• FedLab: A Flexible Federated Learning Framework

  Dun Zeng, Siqi Liang, Xiangjing Hu, Hui Wang, Zenglin Xu, 2023

• Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity

  Artem Vysogorets, Julia Kempe, 2023

• An Analysis of Robustness of Non-Lipschitz Networks

  Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang, 2023

• Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation

  Xu Han, Xiaohui Chen, Francisco J. R. Ruiz, Li-Ping Liu, 2023

• Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization

  Jianhao Ma, Salar Fattahi, 2023

• Statistical Inference for Noisy Incomplete Binary Matrix

  Yunxiao Chen, Chengcheng Li, Jing Ouyang, Gongjun Xu, 2023

• Faith-Shap: The Faithful Shapley Interaction Index

  Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar, 2023

• Decentralized Learning: Theoretical Optimality and Practical Improvements

  Yucheng Lu, Christopher De Sa, 2023

• Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption

  Lihu Xu, Fang Yao, Qiuran Yao, Huiming Zhang, 2023

• Recursive Quantile Estimation: Non-Asymptotic Confidence Bounds

  Likai Chen, Georg Keilbar, Wei Biao Wu, 2023

• Outlier-Robust Subsampling Techniques for Persistent Homology

  Bernadette J. Stolz, 2023

• Neural Operator: Learning Maps Between Function Spaces With Applications to PDEs

  Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar, 2023

• Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data

  Yuqi Gu, Elena E. Erosheva, Gongjun Xu, David B. Dunson, 2023

• Gaussian Processes with Errors in Variables: Theory and Computation

  Shuang Zhou, Debdeep Pati, Tianying Wang, Yun Yang, Raymond J. Carroll, 2023

• Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces

  George Stepaniants, 2023

• Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence

  Henry Lam, Haofeng Zhang, 2023

• Online Optimization over Riemannian Manifolds

  Xi Wang, Zhipeng Tu, Yiguang Hong, Yingyi Wu, Guodong Shi, 2023

• Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees

  William J. Wilkinson, Simo Särkkä, Arno Solin, 2023

• Iterated Block Particle Filter for High-dimensional Parameter Learning: Beating the Curse of Dimensionality

  Ning Ning, Edward L. Ionides, 2023

• Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics

  Gaetano Romano, Idris A. Eckley, Paul Fearnhead, Guillem Rigaill, 2023

• Temporal Abstraction in Reinforcement Learning with the Successor Representation

  Marlos C. Machado, Andre Barreto, Doina Precup, Michael Bowling, 2023

• Approximate Post-Selective Inference for Regression with the Group LASSO

  Snigdha Panigrahi, Peter W MacDonald, Daniel Kessler, 2023

• Towards Learning to Imitate from a Single Video Demonstration

  Glen Berseth, Florian Golemo, Christopher Pal, 2023

• A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models

  Minwoo Chae, Dongha Kim, Yongdai Kim, Lizhen Lin, 2023

• A Randomized Subspace-based Approach for Dimensionality Reduction and Important Variable Selection

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