JMLR Volume 26
- Efficiently Escaping Saddle Points in Bilevel Optimization
- Minhui Huang, Xuxing Chen, Kaiyi Ji, Shiqian Ma, Lifeng Lai; (1):1−61, 2025.
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- 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.
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- 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.
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- Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization
- Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis; (4):1−68, 2025.
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- 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.
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- Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agents
- Marco Pleines, Matthias Pallasch, Frank Zimmer, Mike Preuss; (6):1−40, 2025.
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[code]
- A Random Matrix Approach to Low-Multilinear-Rank Tensor Approximation
- Hugo Lebeau, Florent Chatelain, Romain Couillet; (7):1−64, 2025.
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- 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.
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[code]
- 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.
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[code]
- An Axiomatic Definition of Hierarchical Clustering
- Ery Arias-Castro, Elizabeth Coda; (10):1−26, 2025.
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- Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization
- Tianyi Lin, Chi Jin, Michael I. Jordan; (11):1−45, 2025.
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- Selective Inference with Distributed Data
- Sifan Liu, Snigdha Panigrahi; (12):1−44, 2025.
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[code]
- Estimating Network-Mediated Causal Effects via Principal Components Network Regression
- Alex Hayes, Mark M. Fredrickson, Keith Levin; (13):1−99, 2025.
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[code]
- Locally Private Causal Inference for Randomized Experiments
- Yuki Ohnishi, Jordan Awan; (14):1−40, 2025.
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- 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.
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- Error estimation and adaptive tuning for unregularized robust M-estimator
- Pierre C. Bellec, Takuya Koriyama; (16):1−40, 2025.
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- Supervised Learning with Evolving Tasks and Performance Guarantees
- Verónica Álvarez, Santiago Mazuelas, Jose A. Lozano; (17):1−59, 2025.
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[code]
- Riemannian Bilevel Optimization
- Jiaxiang Li, Shiqian Ma; (18):1−44, 2025.
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[code]
- Random ReLU Neural Networks as Non-Gaussian Processes
- Rahul Parhi, Pakshal Bohra, Ayoub El Biari, Mehrsa Pourya, Michael Unser; (19):1−31, 2025.
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- Regularizing Hard Examples Improves Adversarial Robustness
- Hyungyu Lee, Saehyung Lee, Ho Bae, Sungroh Yoon; (20):1−48, 2025.
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- Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions
- Dapeng Yao, Fangzheng Xie, Yanxun Xu; (21):1−50, 2025.
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- 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.
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[code]
- Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick
- Xiyuan Wang, Pan Li, Muhan Zhang; (23):1−44, 2025.
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[code]
- The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise
- Shuze Daniel Liu, Shuhang Chen, Shangtong Zhang; (24):1−76, 2025.
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- 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. (Machine Learning Open Source Software Paper)
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[code]
- The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond
- Jiin Woo, Gauri Joshi, Yuejie Chi; (26):1−85, 2025.
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- 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.
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[code]
- Optimal Experiment Design for Causal Effect Identification
- Sina Akbari, Jalal Etesami, Negar Kiyavash; (28):1−56, 2025.
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[code]
- Orthogonal Bases for Equivariant Graph Learning with Provable k-WL Expressive Power
- Jia He, Maggie Cheng; (29):1−35, 2025.
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- Bayesian Multi-Group Gaussian Process Models for Heterogeneous Group-Structured Data
- Didong Li, Andrew Jones, Sudipto Banerjee, Barbara E. Engelhardt; (30):1−34, 2025.
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[code]
- Accelerating optimization over the space of probability measures
- Shi Chen, Qin Li, Oliver Tse, Stephen J. Wright; (31):1−40, 2025.
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- Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds
- Clément Bonet, Lucas Drumetz, Nicolas Courty; (32):1−76, 2025.
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[code]
- Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming
- Sen Na, Michael Mahoney; (33):1−75, 2025.
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- 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. (Machine Learning Open Source Software Paper)
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[code]
- Rank-one Convexification for Sparse Regression
- Alper Atamturk, Andres Gomez; (35):1−50, 2025.
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- Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding
- Jiajing Zheng, Alexander D'Amour, Alexander Franks; (36):1−60, 2025.
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[code]
- 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.
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- Optimizing Data Collection for Machine Learning
- Rafid Mahmood, James Lucas, Jose M. Alvarez, Sanja Fidler, Marc T. Law; (38):1−52, 2025.
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- Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning
- Kuangyu Ding, Jingyang Li, Kim-Chuan Toh; (39):1−44, 2025.
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- 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.
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[code]
- On the Approximation of Kernel functions
- Paul Dommel, Alois Pichler; (41):1−30, 2025.
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- Extremal graphical modeling with latent variables via convex optimization
- Sebastian Engelke, Armeen Taeb; (42):1−68, 2025.
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[code]
- Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models
- Xuefeng Gao, Hoang M. Nguyen, Lingjiong Zhu; (43):1−54, 2025.
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- 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.
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- Manifold Fitting under Unbounded Noise
- Zhigang Yao, Yuqing Xia; (45):1−55, 2025.
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- 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.
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- 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.
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[code]
- 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.
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[code]
- 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.
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- 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. (Machine Learning Open Source Software Paper)
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[code]
- Composite Goodness-of-fit Tests with Kernels
- Oscar Key, Arthur Gretton, François-Xavier Briol, Tamara Fernandez; (51):1−60, 2025.
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[code]
- Curvature-based Clustering on Graphs
- Yu Tian, Zachary Lubberts, Melanie Weber; (52):1−67, 2025.
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- 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.
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[code]
- 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. (Machine Learning Open Source Software Paper)
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[code]
- A Comparative Evaluation of Quantification Methods
- Tobias Schumacher, Markus Strohmaier, Florian Lemmerich; (55):1−54, 2025.
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[code]
- Scaling ResNets in the Large-depth Regime
- Pierre Marion, Adeline Fermanian, Gérard Biau, Jean-Philippe Vert; (56):1−48, 2025.
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[code]
- Variance-Aware Estimation of Kernel Mean Embedding
- Geoffrey Wolfer, Pierre Alquier; (57):1−48, 2025.
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- 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.
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- On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations
- Antoine Godichon-Baggioni, Nicklas Werge; (59):1−49, 2025.
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- Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings
- Henrik von Kleist, Alireza Zamanian, Ilya Shpitser, Narges Ahmidi; (60):1−84, 2025.
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- Recursive Causal Discovery
- Ehsan Mokhtarian, Sepehr Elahi, Sina Akbari, Negar Kiyavash; (61):1−65, 2025.
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[code]
- Continuously evolving rewards in an open-ended environment
- Richard M. Bailey; (62):1−51, 2025.
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- 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. (Machine Learning Open Source Software Paper)
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[code]
- Estimation of Local Geometric Structure on Manifolds from Noisy Data
- Yariv Aizenbud, Barak Sober; (64):1−89, 2025.
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[code]
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