JMLR 2025

309 papers

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

Lesi Chen, Yaohua Ma, Jingzhao Zhang

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

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

Vincent Roulet, Siddhartha Srinivasa, Maryam Fazel, Zaïd Harchaoui

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

Lei Wang, Le Bao, Xin Liu

Learning conditional distributions on continuous spaces

Cyril Benezet, Ziteng Cheng, Sebastian Jaimungal

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

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

Rocco Caprio, Juan Kuntz, Samuel Power, Adam M. Johansen

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

Gaoyu Wu, Jelena Bradic, Kean Ming Tan, Wen-Xin Zhou

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

Antoine Chatalic, Nicolas Schreuder, Ernesto De Vito, Lorenzo Rosasco

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

Florian Brück, Jean-David Fermanian, Aleksey Min

Statistical field theory for Markov decision processes under uncertainty

George Stamatescu

Bayesian Data Sketching for Varying Coefficient Regression Models

Rajarshi Guhaniyogi, Laura Baracaldo, Sudipto Banerjee

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

Hanyuan Hang

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

David Bolin, Vaibhav Mehandiratta, Alexandre B. Simas

Invariant Subspace Decomposition

Margherita Lazzaretto, Jonas Peters, Niklas Pfister

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

Insung Kong, Yongdai Kim

Outlier Robust and Sparse Estimation of Linear Regression Coefficients

Takeyuki Sasai, Hironori Fujisawa

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

Sebastian Krämer

Causal Effect of Functional Treatment

Ruoxu Tan, Wei Huang, Zheng Zhang, Guosheng Yin

Uplift Model Evaluation with Ordinal Dominance Graphs

Brecht Verbeken, Marie-Anne Guerry, Wouter Verbeke, Sam Verboven

High-Dimensional L2-Boosting: Rate of Convergence

Ye Luo, Martin Spindler, Jannis Kueck

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

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

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

Seunghoon Paik, Michael Celentano, Alden Green, Ryan J. Tibshirani

On Inference for the Support Vector Machine

Jakub Rybak, Heather Battey, Wen-Xin Zhou

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

Hongru Yang, Yingbin Liang, Xiaojie Guo, Lingfei Wu, Zhangyang Wang

Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability

Atticus Geiger, Duligur Ibeling, Amir Zur, Maheep Chaudhary, Sonakshi Chauhan

Implicit vs Unfolded Graph Neural Networks

Yongyi Yang, Tang Liu, Yangkun Wang, zengfeng Huang, David Wipf

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

Brendon G. Anderson, Ziye Ma, Jingqi Li, Somayeh Sojoudi

GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia

Carlo Lucibello, Aurora Rossi

Dynamic angular synchronization under smoothness constraints

Ernesto Araya, Mihai Cucuringu, Hemant Tyagi

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

Lianghao Cao, Thomas O'Leary-Roseberry, Omar Ghattas

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

Haoshu Xu, Hongzhe Li

Distributed Stochastic Bilevel Optimization: Improved Complexity and Heterogeneity Analysis

Youcheng Niu, Jinming Xu, Ying Sun, Yan Huang, Li Chai

Learning causal graphs via nonlinear sufficient dimension reduction

Eftychia Solea, Bing Li, Kyongwon Kim

On Consistent Bayesian Inference from Synthetic Data

Ossi Räisä, Joonas Jälkö, Antti Honkela

Optimization Over a Probability Simplex

James Chok, Geoffrey M. Vasil

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

Ryan J. Tibshirani, Samy Wu Fung, Howard Heaton, Stanley Osher

Sampling and Estimation on Manifolds using the Langevin Diffusion

Karthik Bharath, Alexander Lewis, Akash Sharma, Michael V. Tretyakov

Sharp Bounds for Sequential Federated Learning on Heterogeneous Data

Yipeng Li, Xinchen Lyu

Local Linear Recovery Guarantee of Deep Neural Networks at Overparameterization

Yaoyu Zhang, Leyang Zhang, Zhongwang Zhang, Zhiwei Bai

Stabilizing Sharpness-Aware Minimization Through A Simple Renormalization Strategy

Chengli Tan, Jiangshe Zhang, Junmin Liu, Yicheng Wang, Yunda Hao

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

Feifei Wang, Zimeng Zhao, Ruimin Ye, Xiaoge Gu, Xiaoling Lu

Deletion Robust Non-Monotone Submodular Maximization over Matroids

Paul Dütting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam

Instability, Computational Efficiency and Statistical Accuracy

nhật Hồ, Koulik Khamaru, Raaz Dwivedi, Martin J. Wainwright, Michael I. Jordan

Estimation of Local Geometric Structure on Manifolds from Noisy Data

Yariv Aizenbud, Barak Sober

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

Continuously evolving rewards in an open-ended environment

Richard M. Bailey

Recursive Causal Discovery

Ehsan Mokhtarian, Sepehr Elahi, Sina Akbari, Negar Kiyavash

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

Henrik Von Kleist, Alireza Zamanian, Ilya Shpitser, Narges Ahmidi

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

Antoine Godichon-Baggioni, Nicklas Werge

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

Yang Chen, Cheng-Der Fuh, Chu-Lan Michael Kao

Variance-Aware Estimation of Kernel Mean Embedding

Geoﬀrey Wolfer, Pierre Alquier

Scaling ResNets in the Large-depth Regime

Pierre Marion, Adeline Fermanian, Gérard Biau, Jean-philippe Vert

A Comparative Evaluation of Quantification Methods

Tobias Schumacher, Markus Strohmaier, Florian Lemmerich

Lightning UQ Box: Uncertainty Quantification for Neural Networks

Nils Lehmann, Nina Maria Gottschling, Jakob Gawlikowski, Adam J. Stewart, Stefan Depeweg

Scaling Data-Constrained Language Models

Niklas Muennighoff, Alexander M. Rush, Boaz Barak, Teven Le Scao, Aleksandra Piktus

Curvature-based Clustering on Graphs

Yu Tian, Zachary Lubberts, Melanie Weber

Composite Goodness-of-fit Tests with Kernels

Oscar Key, Arthur Gretton, François-Xavier Briol, Tamara Fernandez

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

Jianqing Zhang, Yang Liu, Yang Hua, Hao Wang, Tao Song

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

Nikhil Ghosh, Spencer Frei, Wooseok Ha, Bin Yu

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

Pan Zhao, Julie Josse, Shu Yang

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

Xiangdong Xie, Jiahua Guo, Yi Sun

Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization

Shouri Hu, Haowei Wang, Zhongxiang Dai, Bryan Kian Hsiang Low, Szu Hui Ng

Manifold Fitting under Unbounded Noise

Zhigang Yao, Yuqing Xia

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

Zelai Xu, Chao Yu, Yancheng Liang, Yi Wu, Yu Wang

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

Xuefeng GAO, Hoang M. Nguyen, Lingjiong Zhu

Extremal graphical modeling with latent variables via convex optimization

Sebastian Engelke, Armeen Taeb

On the Approximation of Kernel functions

Paul Dommel, Alois Pichler

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

Jue Hou, Rajarshi Mukherjee, Tianxi Cai

Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning

Kuangyu Ding, Jingyang Li, Kim-Chuan Toh

Optimizing Data Collection for Machine Learning

Rafid Mahmood, James Lucas, Jose M. Alvarez, Sanja Fidler, Marc T. Law

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

Shayan Hundrieser, Florian Heinemann, Marcel Klatt, Marina Struleva, Axel Munk

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

Jiajing Zheng, Alexander D'Amour, Alexander Franks

Rank-one Convexification for Sparse Regression

Alper Atamturk, Andres Gomez

gsplat: An Open-Source Library for Gaussian Splatting

Vickie Ye, Ruilong Li, Justin Kerr, Matias Turkulainen, Brent Yi

Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming

Sen Na, Michael Mahoney

Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds

Clément Bonet, Lucas Drumetz, Nicolas Courty

Accelerating optimization over the space of probability measures

Shi Chen, Qin Li, Oliver Tse, Stephen J. Wright

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

Didong Li, Andrew Jones, Sudipto Banerjee, Barbara E. Engelhardt

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

Jia He, Maggie Cheng

Optimal Experiment Design for Causal Effect Identification

Sina Akbari, Jalal Etesami, Negar Kiyavash

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

Jie Peng, Weiyu Li, Stefan Vlaski, Qing Ling

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

Jiin Woo, Gauri Joshi, Yuejie Chi

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

Kaichao You, Runsheng Bai, Meng Cao, Jianmin Wang, Ion Stoica

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

Shuze Daniel Liu, Shuhang Chen, Shangtong Zhang

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

Xiyuan Wang, Pan Li, Muhan Zhang

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

Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions

Dapeng Yao, Fangzheng Xie, Yanxun Xu

Regularizing Hard Examples Improves Adversarial Robustness

HyunGyu Lee, Saehyung Lee, Ho Bae, Sungroh Yoon

Random ReLU Neural Networks as Non-Gaussian Processes

Rahul Parhi, Pakshal Bohra, Ayoub El Biari, Mehrsa Pourya, Michael Unser

Riemannian Bilevel Optimization

Jiaxiang Li, Shiqian Ma

Supervised Learning with Evolving Tasks and Performance Guarantees

Veronica Alvarez, Santiago Mazuelas, Jose A. Lozano

Error estimation and adaptive tuning for unregularized robust M-estimator

Pierre C. Bellec, Takuya Koriyama

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

Locally Private Causal Inference for Randomized Experiments

Yuki Ohnishi, Jordan Awan

Estimating Network-Mediated Causal Effects via Principal Components Network Regression

Alex Hayes, Mark M. Fredrickson, Keith Levin

Selective Inference with Distributed Data

Sifan Liu, Snigdha Panigrahi

Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization

Tianyi Lin, Chi Jin, Michael I. Jordan

An Axiomatic Definition of Hierarchical Clustering

Ery Arias-Castro, Elizabeth Coda