Journal of Machine Learning Research

Efficiently Escaping Saddle Points in Bilevel Optimization

Minhui Huang, Xuxing Chen, Kaiyi Ji, Shiqian Ma, Lifeng Lai (1):1−61, 2025 PDF BibTeX

Bayes Meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes

Charles Riou, Pierre Alquier, Badr-Eddine Chérief-Abdellatif (2):1−60, 2025 PDF BibTeX

DisC2o-HD: Distributed causal inference with covariates shift for analyzing real-world high-dimensional data

Jiayi Tong, Jie Hu, George Hripcsak, Yang Ning, Yong Chen (3):1−50, 2025 PDF BibTeX

Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization

Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis (4):1−68, 2025 PDF BibTeX

Enhancing Graph Representation Learning with Localized Topological Features

Zuoyu Yan, Qi Zhao, Ze Ye, Tengfei Ma, Liangcai Gao, Zhi Tang, Yusu Wang, Chao Chen (5):1−36, 2025 PDF BibTeX

Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agents

Marco Pleines, Matthias Pallasch, Frank Zimmer, Mike Preuss (6):1−40, 2025 code PDF BibTeX

A Random Matrix Approach to Low-Multilinear-Rank Tensor Approximation

Hugo Lebeau, Florent Chatelain, Romain Couillet (7):1−64, 2025 PDF BibTeX

Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback

Boxin Zhao, Lingxiao Wang, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Chaochao Chen, Mladen Kolar (8):1−67, 2025 code PDF BibTeX

Test-Time Training on Video Streams

Renhao Wang, Yu Sun, Arnuv Tandon, Yossi Gandelsman, Xinlei Chen, Alexei A. Efros, Xiaolong Wang (9):1−29, 2025 code PDF BibTeX

An Axiomatic Definition of Hierarchical Clustering

Ery Arias-Castro, Elizabeth Coda (10):1−26, 2025 PDF BibTeX

Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization

Tianyi Lin, Chi Jin, Michael I. Jordan (11):1−45, 2025 PDF BibTeX

Selective Inference with Distributed Data

Sifan Liu, Snigdha Panigrahi (12):1−44, 2025 code PDF BibTeX

Estimating Network-Mediated Causal Effects via Principal Components Network Regression

Alex Hayes, Mark M. Fredrickson, Keith Levin (13):1−99, 2025 code PDF BibTeX

Locally Private Causal Inference for Randomized Experiments

Yuki Ohnishi, Jordan Awan (14):1−40, 2025 PDF BibTeX

From Sparse to Dense Functional Data in High Dimensions: Revisiting Phase Transitions from a Non-Asymptotic Perspective

Shaojun Guo, Dong Li, Xinghao Qiao, Yizhu Wang (15):1−40, 2025 PDF BibTeX

Error estimation and adaptive tuning for unregularized robust M-estimator

Pierre C. Bellec, Takuya Koriyama (16):1−40, 2025 PDF BibTeX

Supervised Learning with Evolving Tasks and Performance Guarantees

Verónica Álvarez, Santiago Mazuelas, Jose A. Lozano (17):1−59, 2025 code PDF BibTeX

Riemannian Bilevel Optimization

Jiaxiang Li, Shiqian Ma (18):1−44, 2025 code PDF BibTeX

Random ReLU Neural Networks as Non-Gaussian Processes

Rahul Parhi, Pakshal Bohra, Ayoub El Biari, Mehrsa Pourya, Michael Unser (19):1−31, 2025 PDF BibTeX

Regularizing Hard Examples Improves Adversarial Robustness

Hyungyu Lee, Saehyung Lee, Ho Bae, Sungroh Yoon (20):1−48, 2025 PDF BibTeX

Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions

Dapeng Yao, Fangzheng Xie, Yanxun Xu (21):1−50, 2025 PDF BibTeX

Directed Cyclic Graphs for Simultaneous Discovery of Time-Lagged and Instantaneous Causality from Longitudinal Data Using Instrumental Variables

Wei Jin, Yang Ni, Amanda B. Spence, Leah H. Rubin, Yanxun Xu (22):1−62, 2025 code PDF BibTeX

JMLR Volume 26

Efficiently Escaping Saddle Points in Bilevel Optimization

Bayes Meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes

DisC2o-HD: Distributed causal inference with covariates shift for analyzing real-world high-dimensional data

Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization

Enhancing Graph Representation Learning with Localized Topological Features

Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agents

A Random Matrix Approach to Low-Multilinear-Rank Tensor Approximation

Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback

Test-Time Training on Video Streams

An Axiomatic Definition of Hierarchical Clustering

Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization

Selective Inference with Distributed Data

Estimating Network-Mediated Causal Effects via Principal Components Network Regression

Locally Private Causal Inference for Randomized Experiments

From Sparse to Dense Functional Data in High Dimensions: Revisiting Phase Transitions from a Non-Asymptotic Perspective

Error estimation and adaptive tuning for unregularized robust M-estimator

Supervised Learning with Evolving Tasks and Performance Guarantees

Riemannian Bilevel Optimization

Random ReLU Neural Networks as Non-Gaussian Processes

Regularizing Hard Examples Improves Adversarial Robustness

Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions

Directed Cyclic Graphs for Simultaneous Discovery of Time-Lagged and Instantaneous Causality from Longitudinal Data Using Instrumental Variables

Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick

The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise

depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers

The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond

Mean Aggregator is More Robust than Robust Aggregators under Label Poisoning Attacks on Distributed Heterogeneous Data

Optimal Experiment Design for Causal Effect Identification

Orthogonal Bases for Equivariant Graph Learning with Provable k-WL Expressive Power

Bayesian Multi-Group Gaussian Process Models for Heterogeneous Group-Structured Data

Accelerating optimization over the space of probability measures

Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds

Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming

gsplat: An Open-Source Library for Gaussian Splatting