JMLR Volume 26

• 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 codePDF 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 codePDF 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 codePDF 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 codePDF BibTeX

• Estimating Network-Mediated Causal Effects via Principal Components Network Regression

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

• Riemannian Bilevel Optimization

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

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

  Xiyuan Wang, Pan Li, Muhan Zhang (23):1−44, 2025 codePDF BibTeX

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

  Shuze Daniel Liu, Shuhang Chen, Shangtong Zhang (24):1−76, 2025 PDF BibTeX

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

  Kaichao You, Runsheng Bai, Meng Cao, Jianmin Wang, Ion Stoica, Mingsheng Long (25):1−18, 2025 codePDF BibTeX

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

  Jiin Woo, Gauri Joshi, Yuejie Chi (26):1−85, 2025 PDF BibTeX

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

  Jie Peng, Weiyu Li, Stefan Vlaski, Qing Ling (27):1−51, 2025 codePDF BibTeX

• Optimal Experiment Design for Causal Effect Identification

  Sina Akbari, Jalal Etesami, Negar Kiyavash (28):1−56, 2025 codePDF BibTeX

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

  Jia He, Maggie Cheng (29):1−35, 2025 PDF BibTeX

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

  Didong Li, Andrew Jones, Sudipto Banerjee, Barbara E. Engelhardt (30):1−34, 2025 codePDF BibTeX

• Accelerating optimization over the space of probability measures

  Shi Chen, Qin Li, Oliver Tse, Stephen J. Wright (31):1−40, 2025 PDF BibTeX

• Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds

  Clément Bonet, Lucas Drumetz, Nicolas Courty (32):1−76, 2025 codePDF BibTeX

• Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming

  Sen Na, Michael Mahoney (33):1−75, 2025 PDF BibTeX

• gsplat: An Open-Source Library for Gaussian Splatting

  Vickie Ye, Ruilong Li, Justin Kerr, Matias Turkulainen, Brent Yi, Zhuoyang Pan, Otto Seiskari, Jianbo Ye, Jeffrey Hu, Matthew Tancik, Angjoo Kanazawa (34):1−17, 2025 codePDF BibTeX

• Rank-one Convexification for Sparse Regression

  Alper Atamturk, Andres Gomez (35):1−50, 2025 PDF BibTeX

• Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding

  Jiajing Zheng, Alexander D'Amour, Alexander Franks (36):1−60, 2025 codePDF BibTeX

• Unbalanced Kantorovich-Rubinstein distance, plan, and barycenter on nite spaces: A statistical perspective

  Shayan Hundrieser, Florian Heinemann, Marcel Klatt, Marina Struleva, Axel Munk (37):1−70, 2025 PDF BibTeX

• Optimizing Data Collection for Machine Learning

  Rafid Mahmood, James Lucas, Jose M. Alvarez, Sanja Fidler, Marc T. Law (38):1−52, 2025 PDF BibTeX

• Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning

  Kuangyu Ding, Jingyang Li, Kim-Chuan Toh (39):1−44, 2025 PDF BibTeX

• Efficient and Robust Semi-supervised Estimation of Average Treatment Effect with Partially Annotated Treatment and Response

  Jue Hou, Rajarshi Mukherjee, Tianxi Cai (40):1−77, 2025 codePDF BibTeX

• On the Approximation of Kernel functions

  Paul Dommel, Alois Pichler (41):1−30, 2025 PDF BibTeX

• Extremal graphical modeling with latent variables via convex optimization

  Sebastian Engelke, Armeen Taeb (42):1−68, 2025 codePDF BibTeX

• Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models

  Xuefeng Gao, Hoang M. Nguyen, Lingjiong Zhu (43):1−54, 2025 PDF BibTeX

• Learning Global Nash Equilibrium in Team Competitive Games with Generalized Fictitious Cross-Play

  Zelai Xu, Chao Yu, Yancheng Liang, Yi Wu, Yu Wang (44):1−30, 2025 PDF BibTeX

• Manifold Fitting under Unbounded Noise

  Zhigang Yao, Yuqing Xia (45):1−55, 2025 PDF BibTeX

• Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization

  Shouri Hu, Haowei Wang, Zhongxiang Dai, Bryan Kian Hsiang Low, Szu Hui Ng (46):1−33, 2025 PDF BibTeX

• DAGs as Minimal I-maps for the Induced Models of Causal Bayesian Networks under Conditioning

  Xiangdong Xie, Jiahua Guo, Yi Sun (47):1−62, 2025 codePDF BibTeX

• Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data

  Pan Zhao, Julie Josse, Shu Yang (48):1−54, 2025 codePDF BibTeX

• The Effect of SGD Batch Size on Autoencoder Learning: Sparsity, Sharpness, and Feature Learning

  Nikhil Ghosh, Spencer Frei, Wooseok Ha, Bin Yu (49):1−61, 2025 PDF BibTeX

• PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark

  Jianqing Zhang, Yang Liu, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Jian Cao (50):1−10, 2025 codePDF BibTeX

• Composite Goodness-of-fit Tests with Kernels

  Oscar Key, Arthur Gretton, François-Xavier Briol, Tamara Fernandez (51):1−60, 2025 codePDF BibTeX

• Curvature-based Clustering on Graphs

  Yu Tian, Zachary Lubberts, Melanie Weber (52):1−67, 2025 PDF BibTeX

• Scaling Data-Constrained Language Models

  Niklas Muennighoff, Alexander M. Rush, Boaz Barak, Teven Le Scao, Aleksandra Piktus, Nouamane Tazi, Sampo Pyysalo, Thomas Wolf, Colin Raffel (53):1−66, 2025 codePDF BibTeX

• Lightning UQ Box: Uncertainty Quantification for Neural Networks

  Nils Lehmann, Nina Maria Gottschling, Jakob Gawlikowski, Adam J. Stewart, Stefan Depeweg, Eric Nalisnick (54):1−7, 2025 codePDF BibTeX

• A Comparative Evaluation of Quantification Methods

  Tobias Schumacher, Markus Strohmaier, Florian Lemmerich (55):1−54, 2025 codePDF BibTeX

• Scaling ResNets in the Large-depth Regime

  Pierre Marion, Adeline Fermanian, Gérard Biau, Jean-Philippe Vert (56):1−48, 2025 codePDF BibTeX

• Variance-Aware Estimation of Kernel Mean Embedding

  Geoffrey Wolfer, Pierre Alquier (57):1−48, 2025 PDF BibTeX

• Determine the Number of States in Hidden Markov Models via Marginal Likelihood

  Yang Chen, Cheng-Der Fuh, Chu-Lan Michael Kao (58):1−59, 2025 PDF BibTeX

• On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations

  Antoine Godichon-Baggioni, Nicklas Werge (59):1−49, 2025 PDF BibTeX

• Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings

  Henrik von Kleist, Alireza Zamanian, Ilya Shpitser, Narges Ahmidi (60):1−84, 2025 PDF BibTeX

• Recursive Causal Discovery

  Ehsan Mokhtarian, Sepehr Elahi, Sina Akbari, Negar Kiyavash (61):1−65, 2025 codePDF BibTeX

• Continuously evolving rewards in an open-ended environment

  Richard M. Bailey (62):1−51, 2025 PDF BibTeX

• Ontolearn---A Framework for Large-scale OWL Class Expression Learning in Python

  Caglar Demir, Alkid Baci, N'Dah Jean Kouagou, Leonie Nora Sieger, Stefan Heindorf, Simon Bin, Lukas Blübaum, Alexander Bigerl, Axel-Cyrille Ngonga Ngomo (63):1−6, 2025 codePDF BibTeX

• Estimation of Local Geometric Structure on Manifolds from Noisy Data

  Yariv Aizenbud, Barak Sober (64):1−89, 2025 codePDF BibTeX

• Instability, Computational Efficiency and Statistical Accuracy

  Nhat Ho, Koulik Khamaru, Raaz Dwivedi, Martin J. Wainwright, Michael I. Jordan, Bin Yu (65):1−68, 2025 PDF BibTeX

• Deletion Robust Non-Monotone Submodular Maximization over Matroids

  Paul Dütting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam (66):1−28, 2025 PDF BibTeX

• Fine-Grained Change Point Detection for Topic Modeling with Pitman-Yor Process

  Feifei Wang, Zimeng Zhao, Ruimin Ye, Xiaoge Gu, Xiaoling Lu (67):1−53, 2025 PDF BibTeX

• Stabilizing Sharpness-Aware Minimization Through A Simple Renormalization Strategy

  Chengli Tan, Jiangshe Zhang, Junmin Liu, Yicheng Wang, Yunda Hao (68):1−35, 2025 PDF BibTeX

• Local Linear Recovery Guarantee of Deep Neural Networks at Overparameterization

  Yaoyu Zhang, Leyang Zhang, Zhongwang Zhang, Zhiwei Bai (69):1−30, 2025 PDF BibTeX

• Sharp Bounds for Sequential Federated Learning on Heterogeneous Data

  Yipeng Li, Xinchen Lyu (70):1−55, 2025 codePDF BibTeX

• Sampling and Estimation on Manifolds using the Langevin Diffusion

  Karthik Bharath, Alexander Lewis, Akash Sharma, Michael V. Tretyakov (71):1−50, 2025 PDF BibTeX

• Laplace Meets Moreau: Smooth Approximation to Infimal Convolutions Using Laplace's Method

  Ryan J. Tibshirani, Samy Wu Fung, Howard Heaton, Stanley Osher (72):1−36, 2025 codePDF BibTeX

• Optimization Over a Probability Simplex

  James Chok, Geoffrey M. Vasil (73):1−35, 2025 codePDF BibTeX

• On Consistent Bayesian Inference from Synthetic Data

  Ossi Räisä, Joonas Jälkö, Antti Honkela (74):1−65, 2025 codePDF BibTeX

• Learning causal graphs via nonlinear sufficient dimension reduction

  Eftychia Solea, Bing Li, Kyongwon Kim (75):1−46, 2025 PDF BibTeX

• Distributed Stochastic Bilevel Optimization: Improved Complexity and Heterogeneity Analysis

  Youcheng Niu, Jinming Xu, Ying Sun, Yan Huang, Li Chai (76):1−58, 2025 PDF BibTeX

• Wasserstein F-tests for Frechet regression on Bures-Wasserstein manifolds

  Haoshu Xu, Hongzhe Li (77):1−123, 2025 PDF BibTeX

• Derivative-Informed Neural Operator Acceleration of Geometric MCMC for Infinite-Dimensional Bayesian Inverse Problems

  Lianghao Cao, Thomas O'Leary-Roseberry, Omar Ghattas (78):1−68, 2025 codePDF BibTeX

• Dynamic angular synchronization under smoothness constraints

  Ernesto Araya, Mihai Cucuringu, Hemant Tyagi (79):1−45, 2025 codePDF BibTeX

• GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia

  Carlo Lucibello, Aurora Rossi (80):1−6, 2025 codePDF BibTeX