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

• 2024.02.18: Volume 24 completed; Volume 25 began.
• 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

• Just Wing It: Near-Optimal Estimation of Missing Mass in a Markovian Sequence

  Ashwin Pananjady, Vidya Muthukumar, Andrew Thangaraj, 2024

• Estimating the Replication Probability of Significant Classification Benchmark Experiments

  Daniel Berrar, 2024

• Causal Discovery with Generalized Linear Models through Peeling Algorithms

  Minjie Wang, Xiaotong Shen, Wei Pan, 2024

• Spectral Regularized Kernel Goodness-of-Fit Tests

  Omar Hagrass, Bharath K. Sriperumbudur, Bing Li, 2024

• Matryoshka Policy Gradient for Entropy-Regularized RL: Convergence and Global Optimality

  François G. Ged, Maria Han Veiga, 2024

• Non-Euclidean Monotone Operator Theory and Applications

  Alexander Davydov, Saber Jafarpour, Anton V. Proskurnikov, Francesco Bullo, 2024

• Stochastic Regularized Majorization-Minimization with weakly convex and multi-convex surrogates

  Hanbaek Lyu, 2024

• Pure Differential Privacy for Functional Summaries with a Laplace-like Process

  Haotian Lin, Matthew Reimherr, 2024

• Sparse Recovery With Multiple Data Streams: An Adaptive Sequential Testing Approach

  Weinan Wang, Bowen Gang, Wenguang Sun, 2024

• Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning

  Luofeng Liao, Zuyue Fu, Zhuoran Yang, Yixin Wang, Dingli Ma, Mladen Kolar, Zhaoran Wang, 2024

• Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past

  Nikolaj Thams, Rikke Søndergaard, Sebastian Weichwald, Jonas Peters, 2024

• RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control

  Jonas Eschmann, Dario Albani, Giuseppe Loianno, 2024

• White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is?

  Yaodong Yu, Sam Buchanan, Druv Pai, Tianzhe Chu, Ziyang Wu, Shengbang Tong, Hao Bai, Yuexiang Zhai, Benjamin D. Haeffele, Yi Ma, 2024

• Commutative Scaling of Width and Depth in Deep Neural Networks

  Soufiane Hayou, 2024

• Value-Distributional Model-Based Reinforcement Learning

  Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters, 2024

• Optimistic Search: Change Point Estimation for Large-scale Data via Adaptive Logarithmic Queries

  Solt Kovács, Housen Li, Lorenz Haubner, Axel Munk, Peter Bühlmann, 2024

• PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization

  Qiqi Duan, Guochen Zhou, Chang Shao, Zhuowei Wang, Mingyang Feng, Yuwei Huang, Yajing Tan, Yijun Yang, Qi Zhao, Yuhui Shi, 2024

• Evidence Estimation in Gaussian Graphical Models Using a Telescoping Block Decomposition of the Precision Matrix

  Anindya Bhadra, Ksheera Sagar, David Rowe, Sayantan Banerjee, Jyotishka Datta, 2024

• An Asymptotic Study of Discriminant and Vote-Averaging Schemes for Randomly-Projected Linear Discriminants

  Lama B. Niyazi, Abla Kammoun, Hayssam Dahrouj, Mohamed-Slim Alouini, Tareq Y. Al-Naffouri, 2024

• Learning and scoring Gaussian latent variable causal models with unknown additive interventions

  Armeen Taeb, Juan L. Gamella, Christina Heinze-Deml, Peter Bühlmann, 2024

• Non-splitting Neyman-Pearson Classifiers

  Jingming Wang, Lucy Xia, Zhigang Bao, Xin Tong, 2024

• Studying the Interplay between Information Loss and Operation Loss in Representations for Classification

  Jorge F. Silva, Felipe Tobar, Mario Vicuña, Felipe Cordova, 2024

• skscope: Fast Sparsity-Constrained Optimization in Python

  Zezhi Wang, Junxian Zhu, Xueqin Wang, Jin Zhu, Huiyang Pen, Peng Chen, Anran Wang, Xiaoke Zhang, 2024

• aeon: a Python Toolkit for Learning from Time Series

  Matthew Middlehurst, Ali Ismail-Fawaz, Antoine Guillaume, Christopher Holder, David Guijo-Rubio, Guzal Bulatova, Leonidas Tsaprounis, Lukasz Mentel, Martin Walter, Patrick Schäfer, Anthony Bagnall, 2024

• Compressed and distributed least-squares regression: convergence rates with applications to federated learning

  Constantin Philippenko, Aymeric Dieuleveut, 2024

• Contamination-source based K-sample clustering

  Xavier Milhaud, Denys Pommeret, Yahia Salhi, Pierre Vandekerkhove, 2024

• Measuring Sample Quality in Algorithms for Intractable Normalizing Function Problems

  Bokgyeong Kang, John Hughes, Murali Haran, 2024

• OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research

  Jiaming Ji, Jiayi Zhou, Borong Zhang, Juntao Dai, Xuehai Pan, Ruiyang Sun, Weidong Huang, Yiran Geng, Mickel Liu, Yaodong Yang, 2024

• Random Smoothing Regularization in Kernel Gradient Descent Learning

  Liang Ding, Tianyang Hu, Jiahang Jiang, Donghao Li, Wenjia Wang, Yuan Yao, 2024

• MLRegTest: A Benchmark for the Machine Learning of Regular Languages

  Sam van der Poel, Dakotah Lambert, Kalina Kostyszyn, Tiantian Gao, Rahul Verma, Derek Andersen, Joanne Chau, Emily Peterson, Cody St. Clair, Paul Fodor, Chihiro Shibata, Jeffrey Heinz, 2024

• A tensor factorization model of multilayer network interdependence

  Izabel Aguiar, Dane Taylor, Johan Ugander, 2024

• Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces

  Iskander Azangulov, Andrei Smolensky, Alexander Terenin, Viacheslav Borovitskiy, 2024

• Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case

  Iskander Azangulov, Andrei Smolensky, Alexander Terenin, Viacheslav Borovitskiy, 2024

• On Doubly Robust Inference for Double Machine Learning in Semiparametric Regression

  Oliver Dukes, Stijn Vansteelandt, David Whitney, 2024

• Deep Neural Network Approximation of Invariant Functions through Dynamical Systems

  Qianxiao Li, Ting Lin, Zuowei Shen, 2024

• A Statistical Experimental Design Method for Constructing Deterministic Sensing Matrices for Compressed Sensing

  Youran Qi, Xu He, Tzu-Hsiang Hung, Peter Chien, 2024

• Functional optimal transport: regularized map estimation and domain adaptation for functional data

  Jiacheng Zhu, Aritra Guha, Dat Do, Mengdi Xu, XuanLong Nguyen, Ding Zhao, 2024

• Desiderata for Representation Learning: A Causal Perspective

  Yixin Wang, Michael I. Jordan, 2024

• Accelerated Gradient Tracking over Time-varying Graphs for Decentralized Optimization

  Huan Li, Zhouchen Lin, 2024

• Pearl: A Production-Ready Reinforcement Learning Agent

  Zheqing Zhu, Rodrigo de Salvo Braz, Jalaj Bhandari, Daniel Jiang, Yi Wan, Yonathan Efroni, Liyuan Wang, Ruiyang Xu, Hongbo Guo, Alex Nikulkov, Dmytro Korenkevych, Urun Dogan, Frank Cheng, Zheng Wu, Wanqiao Xu, 2024

• Boundary constrained Gaussian processes for robust physics-informed machine learning of linear partial differential equations

  David Dalton, Alan Lazarus, Hao Gao, Dirk Husmeier, 2024

• Almost Sure Convergence Rates Analysis and Saddle Avoidance of Stochastic Gradient Methods

  Jun Liu, Ye Yuan, 2024

• False discovery proportion envelopes with m-consistency

  Meah Iqraa, Blanchard Gilles, Roquain Etienne, 2024

• Wasserstein Proximal Coordinate Gradient Algorithms

  Rentian Yao, Xiaohui Chen, Yun Yang, 2024

• Concentration and Moment Inequalities for General Functions of Independent Random Variables with Heavy Tails

  Shaojie Li, Yong Liu, 2024

• Random Fully Connected Neural Networks as Perturbatively Solvable Hierarchies

  Boris Hanin, 2024

• On Regularized Radon-Nikodym Differentiation

  Duc Hoan Nguyen, Werner Zellinger, Sergei Pereverzyev, 2024

• pgmpy: A Python Toolkit for Bayesian Networks

  Ankur Ankan, Johannes Textor, 2024

• Recursive Estimation of Conditional Kernel Mean Embeddings

  Ambrus Tamás, Balázs Csanád Csáji, 2024

• Penalized Overdamped and Underdamped Langevin Monte Carlo Algorithms for Constrained Sampling

  Mert Gurbuzbalaban, Yuanhan Hu, Lingjiong Zhu, 2024

• Fast Rates in Pool-Based Batch Active Learning

  Claudio Gentile, Zhilei Wang, Tong Zhang, 2024

• On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis for Parametric Models

  Xuetong Wu, Mingming Gong, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu, 2024

• Mean-Field Approximation of Cooperative Constrained Multi-Agent Reinforcement Learning (CMARL)

  Washim Uddin Mondal, Vaneet Aggarwal, Satish V. Ukkusuri, 2024

• Structured Optimal Variational Inference for Dynamic Latent Space Models

  Peng Zhao, Anirban Bhattacharya, Debdeep Pati, Bani K. Mallick, 2024

• Stable and Consistent Density-Based Clustering via Multiparameter Persistence

  Alexander Rolle, Luis Scoccola, 2024

• Faster Randomized Methods for Orthogonality Constrained Problems

  Boris Shustin, Haim Avron, 2024

• Estimation of Sparse Gaussian Graphical Models with Hidden Clustering Structure

  Meixia Lin, Defeng Sun, Kim-Chuan Toh, Chengjing Wang, 2024

• Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning

  Sarah Rathnam, Sonali Parbhoo, Siddharth Swaroop, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez, 2024

• PromptBench: A Unified Library for Evaluation of Large Language Models

  Kaijie Zhu, Qinlin Zhao, Hao Chen, Jindong Wang, Xing Xie, 2024

• Gaussian Interpolation Flows

  Yuan Gao, Jian Huang, and Yuling Jiao, 2024

• Gaussian Mixture Models with Rare Events

  Xuetong Li, Jing Zhou, Hansheng Wang, 2024

• On the Concentration of the Minimizers of Empirical Risks

  Paul Escande, 2024

• Variance estimation in graphs with the fused lasso

  Oscar Hernan Madrid Padilla, 2024

• Random measure priors in Bayesian recovery from sketches

  Mario Beraha, Stefano Favaro, Matteo Sesia, 2024

• From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs

  Lorenz Richter, Leon Sallandt, Nikolas Nüsken, 2024

• Label Alignment Regularization for Distribution Shift

  Ehsan Imani, Guojun Zhang, Runjia Li, Jun Luo, Pascal Poupart, Philip H.S. Torr, Yangchen Pan, 2024

• Fairness in Survival Analysis with Distributionally Robust Optimization

  Shu Hu, George H. Chen, 2024

• FineMorphs: Affine-Diffeomorphic Sequences for Regression

  Michele Lohr, Laurent Younes, 2024

• Tensor-train methods for sequential state and parameter learning in state-space models

  Yiran Zhao, Tiangang Cui, 2024

• Memory of recurrent networks: Do we compute it right?

  Giovanni Ballarin, Lyudmila Grigoryeva, Juan-Pablo Ortega, 2024

• The Loss Landscape of Deep Linear Neural Networks: a Second-order Analysis

  El Mehdi Achour, François Malgouyres, Sébastien Gerchinovitz, 2024

• High Probability Convergence Bounds for Non-convex Stochastic Gradient Descent with Sub-Weibull Noise

  Liam Madden, Emiliano Dall'Anese, Stephen Becker, 2024

• Euler Characteristic Tools for Topological Data Analysis

  Olympio Hacquard, Vadim Lebovici, 2024

• Depth Degeneracy in Neural Networks: Vanishing Angles in Fully Connected ReLU Networks on Initialization

  Cameron Jakub, Mihai Nica, 2024

• Fortuna: A Library for Uncertainty Quantification in Deep Learning

  Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias Seeger, Andrew Gordon Wilson, Cedric Archambeau, 2024

• Characterization of translation invariant MMD on Rd and connections with Wasserstein distances

  Thibault Modeste, Clément Dombry, 2024

• On the Hyperparameters in Stochastic Gradient Descent with Momentum

  Bin Shi, 2024

• Improved Random Features for Dot Product Kernels

  Jonas Wacker, Motonobu Kanagawa, Maurizio Filippone, 2024

• Regret Analysis of Bilateral Trade with a Smoothed Adversary

  Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Federico Fusco, Stefano Leonardi, 2024

• Invariant Physics-Informed Neural Networks for Ordinary Differential Equations

  Shivam Arora, Alex Bihlo, Francis Valiquette, 2024

• Distribution Learning via Neural Differential Equations: A Nonparametric Statistical Perspective

  Youssef Marzouk, Zhi (Robert) Ren, Sven Wang, Jakob Zech, 2024

• Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression

  Joseph Shenouda, Rahul Parhi, Kangwook Lee, Robert D. Nowak, 2024

• Individual-centered Partial Information in Social Networks

  Xiao Han, Y. X. Rachel Wang, Qing Yang, Xin Tong, 2024

• Data-driven Automated Negative Control Estimation (DANCE): Search for, Validation of, and Causal Inference with Negative Controls

  Erich Kummerfeld, Jaewon Lim, Xu Shi, 2024

• Continuous Prediction with Experts' Advice

  Nicholas J. A. Harvey, Christopher Liaw, Victor S. Portella, 2024

• Memory-Efficient Sequential Pattern Mining with Hybrid Tries

  Amin Hosseininasab, Willem-Jan van Hoeve, Andre A. Cire, 2024

• Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds

  Zhenghao Xu, Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao, 2024

• Split Conformal Prediction and Non-Exchangeable Data

  Roberto I. Oliveira, Paulo Orenstein, Thiago Ramos, João Vitor Romano, 2024

• Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model

  Rashmi Ranjan Bhuyan, Adel Javanmard, Sungchul Kim, Gourab Mukherjee, Ryan A. Rossi, Tong Yu, Handong Zhao, 2024

• Sparse Graphical Linear Dynamical Systems

  Emilie Chouzenoux, Victor Elvira, 2024

• Statistical analysis for a penalized EM algorithm in high-dimensional mixture linear regression model

  Ning Wang, Xin Zhang, Qing Mai, 2024

• Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds

  Hao Liang, Zhi-Quan Luo, 2024

• Low-Rank Matrix Estimation in the Presence of Change-Points

  Lei Shi, Guanghui Wang, Changliang Zou, 2024

• A Framework for Improving the Reliability of Black-box Variational Inference

  Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins, 2024

• Understanding Entropic Regularization in GANs

  Daria Reshetova, Yikun Bai, Xiugang Wu, Ayfer Özgür, 2024

• BenchMARL: Benchmarking Multi-Agent Reinforcement Learning

  Matteo Bettini, Amanda Prorok, Vincent Moens, 2024

• Learning from many trajectories

  Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi, 2024

• Interpretable algorithmic fairness in structured and unstructured data

  Hari Bandi, Dimitris Bertsimas, Thodoris Koukouvinos, Sofie Kupiec, 2024

• FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization

  José A. Carrillo, Nicolás García Trillos, Sixu Li, Yuhua Zhu, 2024

• On the Connection between Lp- and Risk Consistency and its Implications on Regularized Kernel Methods

  Hannes Köhler, 2024

• Pre-trained Gaussian Processes for Bayesian Optimization

  Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani, 2024

• Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis

  Yuanxing Chen, Qingzhao Zhang, Shuangge Ma, Kuangnan Fang, 2024

• From Small Scales to Large Scales: Distance-to-Measure Density based Geometric Analysis of Complex Data

  Katharina Proksch, Christoph Alexander Weikamp, Thomas Staudt, Benoit Lelandais, Christophe Zimmer, 2024

• PAMI: An Open-Source Python Library for Pattern Mining

  Uday Kiran Rage, Veena Pamalla, Masashi Toyoda, Masaru Kitsuregawa, 2024

• Law of Large Numbers and Central Limit Theorem for Wide Two-layer Neural Networks: The Mini-Batch and Noisy Case

  Arnaud Descours, Arnaud Guillin, Manon Michel, Boris Nectoux, 2024

• Risk Measures and Upper Probabilities: Coherence and Stratification

  Christian Fröhlich, Robert C. Williamson, 2024

• Parallel-in-Time Probabilistic Numerical ODE Solvers

  Nathanael Bosch, Adrien Corenflos, Fatemeh Yaghoobi, Filip Tronarp, Philipp Hennig, Simo Särkkä, 2024

• Scalable High-Dimensional Multivariate Linear Regression for Feature-Distributed Data

  Shuo-Chieh Huang, Ruey S. Tsay, 2024

• Dropout Regularization Versus l2-Penalization in the Linear Model

  Gabriel Clara, Sophie Langer, Johannes Schmidt-Hieber, 2024

• Efficient Convex Algorithms for Universal Kernel Learning

  Aleksandr Talitckii, Brendon Colbert, Matthew M. Peet, 2024

• Manifold Learning by Mixture Models of VAEs for Inverse Problems

  Giovanni S. Alberti, Johannes Hertrich, Matteo Santacesaria, Silvia Sciutto, 2024

• An Algorithmic Framework for the Optimization of Deep Neural Networks Architectures and Hyperparameters

  Julie Keisler, El-Ghazali Talbi, Sandra Claudel, Gilles Cabriel, 2024

• Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity

  Laixi Shi, Yuejie Chi, 2024

• Grokking phase transitions in learning local rules with gradient descent

  Bojan Žunkovič, Enej Ilievski, 2024

• Unsupervised Tree Boosting for Learning Probability Distributions

  Naoki Awaya, Li Ma, 2024

• Linear Regression With Unmatched Data: A Deconvolution Perspective

  Mona Azadkia, Fadoua Balabdaoui, 2024

• Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width Limit

  Karl Hajjar, Lénaïc Chizat, Christophe Giraud, 2024

• Sharp analysis of power iteration for tensor PCA

  Yuchen Wu, Kangjie Zhou, 2024

• On the Intrinsic Structures of Spiking Neural Networks

  Shao-Qun Zhang, Jia-Yi Chen, Jin-Hui Wu, Gao Zhang, Huan Xiong, Bin Gu, Zhi-Hua Zhou, 2024

• Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance

  Lisha Chen, Heshan Fernando, Yiming Ying, Tianyi Chen, 2024

• Neural Collapse for Unconstrained Feature Model under Cross-entropy Loss with Imbalanced Data

  Wanli Hong, Shuyang Ling, 2024

• Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables

  Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang, 2024

• Classification of Data Generated by Gaussian Mixture Models Using Deep ReLU Networks

  Tian-Yi Zhou, Xiaoming Huo, 2024

• Differentially Private Topological Data Analysis

  Taegyu Kang, Sehwan Kim, Jinwon Sohn, Jordan Awan, 2024

• On the Optimality of Misspecified Spectral Algorithms

  Haobo Zhang, Yicheng Li, Qian Lin, 2024

• An Entropy-Based Model for Hierarchical Learning

  Amir R. Asadi, 2024

• Optimal Clustering with Bandit Feedback

  Junwen Yang, Zixin Zhong, Vincent Y. F. Tan, 2024

• A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression

  Youngseok Kim, Wei Wang, Peter Carbonetto, Matthew Stephens, 2024

• Spectral Analysis of the Neural Tangent Kernel for Deep Residual Networks

  Yuval Belfer, Amnon Geifman, Meirav Galun, Ronen Basri, 2024

• Permuted and Unlinked Monotone Regression in R^d: an approach based on mixture modeling and optimal transport

  Martin Slawski, Bodhisattva Sen, 2024

• Volterra Neural Networks (VNNs)

  Siddharth Roheda, Hamid Krim, Bo Jiang, 2024

• Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm

  Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton, 2024

• Bayesian Regression Markets

  Thomas Falconer, Jalal Kazempour, Pierre Pinson, 2024

• Sharpness-Aware Minimization and the Edge of Stability

  Philip M. Long, Peter L. Bartlett, 2024

• Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization

  Sijia Chen, Yu-Jie Zhang, Wei-Wei Tu, Peng Zhao, Lijun Zhang, 2024

• Multi-Objective Neural Architecture Search by Learning Search Space Partitions

  Yiyang Zhao, Linnan Wang, Tian Guo, 2024

• Fermat Distances: Metric Approximation, Spectral Convergence, and Clustering Algorithms

  Nicolás García Trillos, Anna Little, Daniel McKenzie, James M. Murphy, 2024

• Spherical Rotation Dimension Reduction with Geometric Loss Functions

  Hengrui Luo, Jeremy E. Purvis, Didong Li, 2024

• A PDE-based Explanation of Extreme Numerical Sensitivities and Edge of Stability in Training Neural Networks

  Yuxin Sun, Dong Lao, Anthony Yezzi, Ganesh Sundaramoorthi, 2024

• Two is Better Than One: Regularized Shrinkage of Large Minimum Variance Portfolios

  Taras Bodnar, Nestor Parolya, Erik Thorsen, 2024

• Decentralized Natural Policy Gradient with Variance Reduction for Collaborative Multi-Agent Reinforcement Learning

  Jinchi Chen, Jie Feng, Weiguo Gao, Ke Wei, 2024

• Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning

  Ilnura Usmanova, Yarden As, Maryam Kamgarpour, Andreas Krause, 2024

• Cluster-Adaptive Network A/B Testing: From Randomization to Estimation

  Yang Liu, Yifan Zhou, Ping Li, Feifang Hu, 2024

• On the Computational and Statistical Complexity of Over-parameterized Matrix Sensing

  Jiacheng Zhuo, Jeongyeol Kwon, Nhat Ho, Constantine Caramanis, 2024

• Optimization-based Causal Estimation from Heterogeneous Environments

  Mingzhang Yin, Yixin Wang, David M. Blei, 2024

• Optimal Locally Private Nonparametric Classification with Public Data

  Yuheng Ma, Hanfang Yang, 2024

• Learning to Warm-Start Fixed-Point Optimization Algorithms

  Rajiv Sambharya, Georgina Hall, Brandon Amos, Bartolomeo Stellato, 2024

• Nonparametric Regression Using Over-parameterized Shallow ReLU Neural Networks

  Yunfei Yang, Ding-Xuan Zhou, 2024

• Nonparametric Copula Models for Multivariate, Mixed, and Missing Data

  Joseph Feldman, Daniel R. Kowal, 2024

• An Analysis of Quantile Temporal-Difference Learning

  Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney, 2024

• Conformal Inference for Online Prediction with Arbitrary Distribution Shifts

  Isaac Gibbs, Emmanuel J. Candès, 2024

• More Efficient Estimation of Multivariate Additive Models Based on Tensor Decomposition and Penalization

  Xu Liu, Heng Lian, Jian Huang, 2024

• A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment

  Robert Hu, Dino Sejdinovic, Robin J. Evans, 2024

• Assessing the Overall and Partial Causal Well-Specification of Nonlinear Additive Noise Models

  Christoph Schultheiss, Peter Bühlmann, 2024

• Simple Cycle Reservoirs are Universal

  Boyu Li, Robert Simon Fong, Peter Tino, 2024

• On the Computational Complexity of Metropolis-Adjusted Langevin Algorithms for Bayesian Posterior Sampling

  Rong Tang, Yun Yang, 2024

• Generalization and Stability of Interpolating Neural Networks with Minimal Width

  Hossein Taheri, Christos Thrampoulidis, 2024

• Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression

  Jiading Liu, Lei Shi, 2024

• Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations

  Yuanyuan Wang, Wei Huang, Mingming Gong, Xi Geng, Tongliang Liu, Kun Zhang, Dacheng Tao, 2024

• Robust Black-Box Optimization for Stochastic Search and Episodic Reinforcement Learning

  Maximilian Hüttenrauch, Gerhard Neumann, 2024

• Kernel Thinning

  Raaz Dwivedi, Lester Mackey, 2024

• Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions

  Xuxing Chen, Tesi Xiao, Krishnakumar Balasubramanian, 2024

• Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks

  Yunpeng Zhao, Ning Hao, Ji Zhu, 2024

• Statistical Inference for Fairness Auditing

  John J. Cherian, Emmanuel J. Candès, 2024

• Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning

  Yiling Xie, Xiaoming Huo, 2024

• DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models

  Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing, 2024

• Flexible Bayesian Product Mixture Models for Vector Autoregressions

  Suprateek Kundu, Joshua Lukemire, 2024

• A Variational Approach to Bayesian Phylogenetic Inference

  Cheng Zhang, Frederick A. Matsen IV, 2024

• Fat-Shattering Dimension of k-fold Aggregations

  Idan Attias, Aryeh Kontorovich, 2024

• Unified Binary and Multiclass Margin-Based Classification

  Yutong Wang, Clayton Scott, 2024

• Neural Feature Learning in Function Space

  Xiangxiang Xu, Lizhong Zheng, 2024

• PyGOD: A Python Library for Graph Outlier Detection

  Kay Liu, Yingtong Dou, Xueying Ding, Xiyang Hu, Ruitong Zhang, Hao Peng, Lichao Sun, Philip S. Yu, 2024

• Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria

  Tengyuan Liang, 2024

• Fixed points of nonnegative neural networks

  Tomasz J. Piotrowski, Renato L. G. Cavalcante, Mateusz Gabor, 2024

• Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks

  Fanghui Liu, Leello Dadi, Volkan Cevher, 2024

• A Survey on Multi-player Bandits

  Etienne Boursier, Vianney Perchet, 2024

• Transport-based Counterfactual Models

  Lucas De Lara, Alberto González-Sanz, Nicholas Asher, Laurent Risser, Jean-Michel Loubes, 2024

• Adaptive Latent Feature Sharing for Piecewise Linear Dimensionality Reduction

  Adam Farooq, Yordan P. Raykov, Petar Raykov, Max A. Little, 2024

• Topological Node2vec: Enhanced Graph Embedding via Persistent Homology

  Yasuaki Hiraoka, Yusuke Imoto, Théo Lacombe, Killian Meehan, Toshiaki Yachimura, 2024

• Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length

  Katerina Hlaváčková-Schindler, Anna Melnykova, Irene Tubikanec, 2024

• Representation Learning via Manifold Flattening and Reconstruction

  Michael Psenka, Druv Pai, Vishal Raman, Shankar Sastry, Yi Ma, 2024

• Bagging Provides Assumption-free Stability

  Jake A. Soloff, Rina Foygel Barber, Rebecca Willett, 2024

• Fairness guarantees in multi-class classification with demographic parity

  Christophe Denis, Romuald Elie, Mohamed Hebiri, François Hu, 2024

• Regimes of No Gain in Multi-class Active Learning

  Gan Yuan, Yunfan Zhao, Samory Kpotufe, 2024

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