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

• Towards Optimal Branching of Linear and Semidefinite Relaxations for Neural Network Robustness Certification

  Brendon G. Anderson, Ziye Ma, Jingqi Li, Somayeh Sojoudi (81):1−59, 2025 PDF BibTeX

• Implicit vs Unfolded Graph Neural Networks

  Yongyi Yang, Tang Liu, Yangkun Wang, Zengfeng Huang, David Wipf (82):1−46, 2025 PDF BibTeX

• Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability

  Atticus Geiger, Duligur Ibeling, Amir Zur, Maheep Chaudhary, Sonakshi Chauhan, Jing Huang, Aryaman Arora, Zhengxuan Wu, Noah Goodman, Christopher Potts, Thomas Icard (83):1−64, 2025 PDF BibTeX

• Random Pruning Over-parameterized Neural Networks Can Improve Generalization: A Training Dynamics Analysis

  Hongru Yang, Yingbin Liang, Xiaojie Guo, Lingfei Wu, Zhangyang Wang (84):1−51, 2025 PDF BibTeX

• On Inference for the Support Vector Machine

  Jakub Rybak, Heather Battey, Wen-Xin Zhou (85):1−54, 2025 PDF BibTeX

• Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Test

  Seunghoon Paik, Michael Celentano, Alden Green, Ryan J. Tibshirani (86):1−57, 2025 codePDF BibTeX

• How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences

  Mikolaj J. Kasprzak, Ryan Giordano, Tamara Broderick (87):1−81, 2025 codePDF BibTeX

• Feature Learning in Finite-Width Bayesian Deep Linear Networks with Multiple Outputs and Convolutional Layers

  Federico Bassetti, Marco Gherardi, Alessandro Ingrosso, Mauro Pastore, Pietro Rotondo (88):1−35, 2025 PDF BibTeX

• High-Dimensional L2-Boosting: Rate of Convergence

  Ye Luo, Martin Spindler, Jannis Kueck (89):1−54, 2025 PDF BibTeX

• Uplift Model Evaluation with Ordinal Dominance Graphs

  Brecht Verbeken, Marie-Anne Guerry, Wouter Verbeke, Sam Verboven (90):1−56, 2025 PDF BibTeX

• Causal Effect of Functional Treatment

  Ruoxu Tan, Wei Huang, Zheng Zhang, Guosheng Yin (91):1−39, 2025 codePDF BibTeX

• Affine Rank Minimization via Asymptotic Log-Det Iteratively Reweighted Least Squares

  Sebastian Krämer (92):1−44, 2025 PDF BibTeX

• Outlier Robust and Sparse Estimation of Linear Regression Coefficients

  Takeyuki Sasai, Hironori Fujisawa (93):1−79, 2025 PDF BibTeX

• Posterior Concentrations of Fully-Connected Bayesian Neural Networks with General Priors on the Weights

  Insung Kong, Yongdai Kim (94):1−60, 2025 PDF BibTeX

• Invariant Subspace Decomposition

  Margherita Lazzaretto, Jonas Peters, Niklas Pfister (95):1−56, 2025 codePDF BibTeX

• Linear cost and exponentially convergent approximation of Gaussian Matérn processes on intervals

  David Bolin, Vaibhav Mehandiratta, Alexandre B. Simas (96):1−34, 2025 codePDF BibTeX

• Bagged k-Distance for Mode-Based Clustering Using the Probability of Localized Level Sets

  Hanyuan Hang (97):1−62, 2025 PDF BibTeX

• Bayesian Data Sketching for Varying Coefficient Regression Models

  Rajarshi Guhaniyogi, Laura Baracaldo, Sudipto Banerjee (98):1−29, 2025 PDF BibTeX

• Statistical field theory for Markov decision processes under uncertainty

  George Stamatescu (99):1−24, 2025 PDF BibTeX

• Distribution Free Tests for Model Selection Based on Maximum Mean Discrepancy with Estimated Parameters

  Florian Brück, Jean-David Fermanian, Aleksey Min (100):1−52, 2025 codePDF BibTeX

• Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling

  Antoine Chatalic, Nicolas Schreuder, Ernesto De Vito, Lorenzo Rosasco (101):1−55, 2025 codePDF BibTeX

• Linear Hypothesis Testing in High-Dimensional Expected Shortfall Regression with Heavy-Tailed Errors

  Gaoyu Wu, Jelena Bradic, Kean Ming Tan, Wen-Xin Zhou (102):1−54, 2025 PDF BibTeX

• Error bounds for particle gradient descent, and extensions of the log-Sobolev and Talagrand inequalities

  Rocco Caprio, Juan Kuntz, Samuel Power, Adam M. Johansen (103):1−38, 2025 PDF BibTeX

• A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs

  Lukas Zierahn, Dirk van der Hoeven, Tal Lancewicki, Aviv Rosenberg, Nicolò Cesa-Bianchi (104):1−60, 2025 codePDF BibTeX

• Learning conditional distributions on continuous spaces

  Cyril Benezet, Ziteng Cheng, Sebastian Jaimungal (105):1−64, 2025 codePDF BibTeX

• A Decentralized Proximal Gradient Tracking Algorithm for Composite Optimization on Riemannian Manifolds

  Lei Wang, Le Bao, Xin Liu (106):1−37, 2025 PDF BibTeX

• On Global and Local Convergence of Iterative Linear Quadratic Optimization Algorithms for Discrete Time Nonlinear Control

  Vincent Roulet, Siddhartha Srinivasa, Maryam Fazel, Zaid Harchaoui (107):1−85, 2025 codePDF BibTeX

• Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos

  Shao-Bo Lin, Xiaotong Liu, Di Wang, Hai Zhang, Ding-Xuan Zhou (108):1−54, 2025 PDF BibTeX

• Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles

  Lesi Chen, Yaohua Ma, Jingzhao Zhang (109):1−56, 2025 PDF BibTeX

• Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms

  Keru Wu, Yuansi Chen, Wooseok Ha, Bin Yu (110):1−92, 2025 codePDF BibTeX

• On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension

  Saptarshi Chakraborty, Peter L. Bartlett (111):1−57, 2025 PDF BibTeX

• Score-based Causal Representation Learning: Linear and General Transformations

  Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Abhishek Kumar, Ali Tajer (112):1−90, 2025 codePDF BibTeX

• Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers

  Fan Yang, Hongyang R. Zhang, Sen Wu, Christopher Re, Weijie J. Su (113):1−88, 2025 codePDF BibTeX

• Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF

  Han Shen, Zhuoran Yang, Tianyi Chen (114):1−49, 2025 PDF BibTeX

• DRM Revisited: A Complete Error Analysis

  Yuling Jiao, Ruoxuan Li, Peiying Wu, Jerry Zhijian Yang, Pingwen Zhang (115):1−76, 2025 PDF BibTeX

• Bayesian Scalar-on-Image Regression with a Spatially Varying Single-layer Neural Network Prior

  Ben Wu, Keru Wu, Jian Kang (116):1−38, 2025 PDF BibTeX

• On Model Identification and Out-of-Sample Prediction of PCR with Applications to Synthetic Controls

  Anish Agarwal, Devavrat Shah, Dennis Shen (117):1−58, 2025 codePDF BibTeX

• Sparse SVM with Hard-Margin Loss: a Newton-Augmented Lagrangian Method in Reduced Dimensions

  Penghe Zhang, Naihua Xiu, Hou-Duo Qi (118):1−55, 2025 PDF BibTeX

• Quantifying the Effectiveness of Linear Preconditioning in Markov Chain Monte Carlo

  Max Hird, Samuel Livingstone (119):1−51, 2025 PDF BibTeX

• Degree of Interference: A General Framework For Causal Inference Under Interference

  Yuki Ohnishi, Bikram Karmakar, Arman Sabbaghi (120):1−37, 2025 PDF BibTeX

• Maximum Causal Entropy IRL in Mean-Field Games and GNEP Framework for Forward RL

  Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi (121):1−40, 2025 PDF BibTeX

• Posterior and Variational Inference for Deep Neural Networks with Heavy-Tailed Weights

  Paul Egels, Ismaël Castillo (122):1−58, 2025 PDF BibTeX

• Last-iterate Convergence of Shuffling Momentum Gradient Method under the Kurdyka-Lojasiewicz Inequality

  Yuqing Liang, Dongpo Xu (123):1−51, 2025 PDF BibTeX

• Physics-informed Kernel Learning

  Nathan Doumèche, Francis Bach, Gérard Biau, Claire Boyer (124):1−39, 2025 codePDF BibTeX

• BitNet: 1-bit Pre-training for Large Language Models

  Hongyu Wang, Shuming Ma, Lingxiao Ma, Lei Wang, Wenhui Wang, Li Dong, Shaohan Huang, Huaijie Wang, Jilong Xue, Ruiping Wang, Yi Wu, Furu Wei (125):1−29, 2025 PDF BibTeX

• Modelling Populations of Interaction Networks via Distance Metrics

  George Bolt, Simón Lunagómez, Christopher Nemeth (126):1−112, 2025 PDF BibTeX

• Actor-Critic learning for mean-field control in continuous time

  Noufel FRIKHA, Maximilien GERMAIN, Mathieu LAURIERE, Huyen PHAM, Xuanye SONG (127):1−42, 2025 PDF BibTeX

• Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning

  Yong Lin, Chen Liu, Chenlu Ye, Qing Lian, Yuan Yao, Tong Zhang (128):1−47, 2025 codePDF BibTeX

• Transformers from Diffusion: A Unified Framework for Neural Message Passing

  Qitian Wu, David Wipf, Junchi Yan (129):1−47, 2025 codePDF BibTeX

• Categorical Semantics of Compositional Reinforcement Learning

  Georgios Bakirtzis, Michail Savvas, Ufuk Topcu (130):1−37, 2025 PDF BibTeX

• On the O(sqrt(d)/T^(1/4)) Convergence Rate of RMSProp and Its Momentum Extension Measured by l_1 Norm

  Huan Li, Yiming Dong, Zhouchen Lin (131):1−25, 2025 codePDF BibTeX

• Score-Aware Policy-Gradient and Performance Guarantees using Local Lyapunov Stability

  Céline Comte, Matthieu Jonckheere, Jaron Sanders, Albert Senen-Cerda (132):1−74, 2025 codePDF BibTeX

• PREMAP: A Unifying PREiMage APproximation Framework for Neural Networks

  Xiyue Zhang, Benjie Wang, Marta Kwiatkowska, Huan Zhang (133):1−44, 2025 PDF BibTeX

• Characterizing Dynamical Stability of Stochastic Gradient Descent in Overparameterized Learning

  Dennis Chemnitz, Maximilian Engel (134):1−46, 2025 PDF BibTeX

• Optimal and Efficient Algorithms for Decentralized Online Convex Optimization

  Yuanyu Wan, Tong Wei, Bo Xue, Mingli Song, Lijun Zhang (135):1−43, 2025 PDF BibTeX

• Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary

  Tianyang Hu, Ruiqi Liu, Zuofeng Shang, Guang Cheng (136):1−38, 2025 PDF BibTeX

• Randomly Projected Convex Clustering Model: Motivation, Realization, and Cluster Recovery Guarantees

  Ziwen Wang, Yancheng Yuan, Jiaming Ma, Tieyong Zeng, Defeng Sun (137):1−57, 2025 PDF BibTeX

• Finite Expression Method for Solving High-Dimensional Partial Differential Equations

  Senwei Liang, Haizhao Yang (138):1−31, 2025 codePDF BibTeX

• Diffeomorphism-based feature learning using Poincaré inequalities on augmented input space

  Romain Verdière, Clémentine Prieur, Olivier Zahm (139):1−31, 2025 PDF BibTeX

• Deep Variational Multivariate Information Bottleneck - A Framework for Variational Losses

  Eslam Abdelaleem, Ilya Nemenman, K. Michael Martini (140):1−50, 2025 codePDF BibTeX

• Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching

  Jannis Chemseddine, Paul Hagemann, Gabriele Steidl, Christian Wald (141):1−47, 2025 codePDF BibTeX

• ClimSim-Online: A Large Multi-Scale Dataset and Framework for Hybrid Physics-ML Climate Emulation

  Sungduk Yu, Zeyuan Hu, Akshay Subramaniam, Walter Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius J. M. Busecke, Nora Loose, Charles I Stern, Tom Beucler, Bryce Harrop, Helge Heuer, Benjamin R Hillman, Andrea Jenney, Nana Liu, Alistair White, Tian Zheng, Zhiming Kuang, Fiaz Ahmed, Elizabeth Barnes, Noah D. Brenowitz, Christopher Bretherton, Veronika Eyring, Savannah Ferretti, Nicholas Lutsko, Pierre Gentine, Stephan Mandt, J. David Neelin, Rose Yu, Laure Zanna, Nathan M. Urban, Janni Yuval, Ryan Abernathey, Pierre Baldi, Wayne Chuang, Yu Huang, Fernando Iglesias-Suarez, Sanket Jantre, Po-Lun Ma, Sara Shamekh, Guang Zhang, Michael Pritchard (142):1−85, 2025 codePDF BibTeX

• Deep Generative Models: Complexity, Dimensionality, and Approximation

  Kevin Wang, Hongqian Niu, Yixin Wang, Didong Li (143):1−37, 2025 codePDF BibTeX

• Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems

  Michal Dereziński, Daniel LeJeune, Deanna Needell, Elizaveta Rebrova (144):1−49, 2025 PDF BibTeX

• On the Ability of Deep Networks to Learn Symmetries from Data: A Neural Kernel Theory

  Andrea Perin, Stephane Deny (145):1−70, 2025 codePDF BibTeX

• Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models

  Sudipto Banerjee, Xiang Chen, Ian Frankenburg, Daniel Zhou (146):1−43, 2025 codePDF BibTeX

• Latent Process Models for Functional Network Data

  Peter W. MacDonald, Elizaveta Levina, Ji Zhu (147):1−69, 2025 codePDF BibTeX

• Losing Momentum in Continuous-time Stochastic Optimisation

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