JMLR 2025

309 papers

Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Michael Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden

Efficient Methods for Non-stationary Online Learning

Peng Zhao, Yan-Feng Xie, Lijun Zhang, Zhi-Hua Zhou

Decentralized Asynchronous Optimization with DADAO allows Decoupling and Acceleration

Adel Nabli, Edouard Oyallon

Mixtures of Gaussian Process Experts with SMC^2

Teemu Härkönen, Sara Wade, Kody Law, Lassi Roininen

Robust Point Matching with Distance Profiles

YoonHaeng Hur, Yuehaw Khoo

BoFire: Bayesian Optimization Framework Intended for Real Experiments

Johannes P. Dürholt, Thomas S. Asche, Johanna Kleinekorte, Gabriel Mancino-Ball, Benjamin Schiller

Reliever: Relieving the Burden of Costly Model Fits for Changepoint Detection

Chengde Qian, Guanghui Wang, Changliang Zou

Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs

Charles C. Margossian, Loucas Pillaud-Vivien, Lawrence K. Saul

Are Ensembles Getting Better All the Time?

Pierre-Alexandre Mattei, Damien Garreau

An Adaptive Parameter-free and Projection-free Restarting Level Set Method for Constrained Convex Optimization Under the Error Bound Condition

Qihang Lin, Negar Soheili, Runchao Ma, Selvaprabu Nadarajah

Operator Learning for Hyperbolic PDEs

Christopher Wang, Alex Townsend

Optimal subsampling for high-dimensional partially linear models via machine learning methods

Yujing Shao, Lei Wang, Heng Lian, HaiYing Wang

Decentralized Sparse Linear Regression via Gradient-Tracking

Marie Maros, Gesualdo Scutari, Ying Sun, Guang Cheng

Calibrated Inference: Statistical Inference that Accounts for Both Sampling Uncertainty and Distributional Uncertainty

Yujin Jeong, Dominik Rothenhäusler

Relaxed Gaussian Process Interpolation: a Goal-Oriented Approach to Bayesian Optimization

Sébastien J. Petit, Julien Bect, Emmanuel Vazquez

Linear Separation Capacity of Self-Supervised Representation Learning

Shulei Wang

On the Convergence of Projected Policy Gradient for Any Constant Step Sizes

Jiacai Liu, Wenye Li, Dachao Lin, Ke Wei, Zhihua Zhang

Learning with Linear Function Approximations in Mean-Field Control

Erhan Bayraktar, Ali Devran Kara

A New Random Reshuffling Method for Nonsmooth Nonconvex Finite-sum Optimization

Junwen Qiu, Xiao Li, Andre Milzarek

Model-free Change-Point Detection Using AUC of a Classifier

Rohit Kanrar, Feiyu Jiang, Zhanrui Cai

EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback

Ilyas Fatkhullin, Igor Sokolov, Eduard Gorbunov, Zhize Li, Peter Richtarik

Multiple Instance Verification

Xin Xu, Eibe Frank, Geoffrey Holmes

Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness

Ye Tian, Yuqi Gu, Yang Feng

Exponential Family Graphical Models: Correlated Replicates and Unmeasured Confounders, with Applications to fMRI Data

Yanxin Jin, Yang Ning, Kean Ming Tan

Optimizing Return Distributions with Distributional Dynamic Programming

Bernardo Avila Pires, Mark Rowland, Diana Borsa, Zhaohan Daniel Guo, Khimya Khetarpal

Imprecise Multi-Armed Bandits: Representing Irreducible Uncertainty as a Zero-Sum Game

Vanessa Kosoy

Early Alignment in Two-Layer Networks Training is a Two-Edged Sword

Etienne Boursier, Nicolas Flammarion

Hierarchical Decision Making Based on Structural Information Principles

Xianghua Zeng, Hao Peng, Dingli Su, Angsheng Li

Generative Adversarial Networks: Dynamics

Matias G. Delgadino, Bruno B. Suassuna, Rene Cabrera

"What is Different Between These Datasets?" A Framework for Explaining Data Distribution Shifts

Varun Babbar*, Zhicheng Guo*, Cynthia Rudin

Assumption-lean and data-adaptive post-prediction inference

Jiacheng Miao, Xinran Miao, Yixuan Wu, Jiwei Zhao, Qiongshi Lu

Bagged Regularized k-Distances for Anomaly Detection

Yuchao Cai, Hanfang Yang, Yuheng Ma, Hanyuan Hang

Four Axiomatic Characterizations of the Integrated Gradients Attribution Method

Daniel Lundstrom, Meisam Razaviyayn

Fast Algorithm for Constrained Linear Inverse Problems

Mohammed Rayyan Sheriff, Floor Fenne Redel, Peyman Mohajerin Esfahani

High-Rank Irreducible Cartesian Tensor Decomposition and Bases of Equivariant Spaces

Shihao Shao, Yikang LI, Zhouchen Lin, Qinghua Cui

Best Linear Unbiased Estimate from Privatized Contingency Tables

Jordan Awan, Adam Edwards, Paul Bartholomew, Andrew Sillers

Interpretable Global Minima of Deep ReLU Neural Networks on Sequentially Separable Data

Thomas Chen, Patrícia Muñoz Ewald

Enhanced Feature Learning via Regularisation: Integrating Neural Networks and Kernel Methods

Bertille FOLLAIN, Francis Bach

Data-Driven Performance Guarantees for Classical and Learned Optimizers

Rajiv Sambharya, Bartolomeo Stellato

Contextual Bandits with Stage-wise Constraints

Aldo Pacchiano, Mohammad Ghavamzadeh, Peter Bartlett

Boosting Causal Additive Models

Maximilian Kertel, Nadja Klein

Frequentist Guarantees of Distributed (Non)-Bayesian Inference

Bohan Wu, César A. Uribe

Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection

Daiqi Gao, Yufeng Liu, Donglin Zeng

EMaP: Explainable AI with Manifold-based Perturbations

Minh Nhat VU, Huy Quang Mai, My T. Thai

Autoencoders in Function Space

Justin Bunker, Mark Girolami, Hefin Lambley, Andrew M. Stuart, T. J. Sullivan

Nonparametric Regression on Random Geometric Graphs Sampled from Submanifolds

Paul Rosa, Judith Rousseau

System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learning

Matteo Bettini, Ajay Shankar, Amanda Prorok

Distribution Estimation under the Infinity Norm

Aryeh Kontorovich, Amichai Painsky

Extending Temperature Scaling with Homogenizing Maps

Christopher Qian, Feng Liang, Jason Adams

Density Estimation Using the Perceptron

Patrik Robert Gerber, Tianze Jiang, Yury Polyanskiy, Rui Sun

Simplex Constrained Sparse Optimization via Tail Screening

Peng Chen, Jin Zhu, Junxian Zhu, Xueqin Wang

Score-Based Diffusion Models in Function Space

Jae Hyun Lim, Nikola B. Kovachki, Ricardo Baptista, Christopher Beckham, Kamyar Azizzadenesheli

Regularized Rényi Divergence Minimization through Bregman Proximal Gradient Algorithms

Thomas Guilmeau, Emilie Chouzenoux, Victor Elvira

WEFE: A Python Library for Measuring and Mitigating Bias in Word Embeddings

Pablo Badilla, Felipe Bravo-Marquez, Maria Jose Zambrano, Jorge Pérez

Frontiers to the learning of nonparametric hidden Markov models

Kweku Abraham, Elisabeth Gassiat, Zacharie Naulet

On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes

Zhiheng Chen, Guanhua Fang, Wen Yu

Classification in the high dimensional Anisotropic mixture framework: A new take on Robust Interpolation

Stanislav Minsker, Mohamed Ndaoud, Yiqiu Shen

Universal Online Convex Optimization Meets Second-order Bounds

Lijun Zhang, Yibo Wang, Guanghui Wang, Jinfeng Yi, Tianbao Yang

Sample Complexity of the Linear Quadratic Regulator: A Reinforcement Learning Lens

Amirreza Neshaei Moghaddam, Alex Olshevsky, Bahman Gharesifard

Randomization Can Reduce Both Bias and Variance: A Case Study in Random Forests

Brian Liu, Rahul Mazumder

skglm: Improving scikit-learn for Regularized Generalized Linear Models

Badr MOUFAD, Pierre-Antoine Bannier, Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias

Losing Momentum in Continuous-time Stochastic Optimisation

Kexin Jin, Jonas Latz, Chenguang Liu, Alessandro Scagliotti

Latent Process Models for Functional Network Data

Peter W. MacDonald, Elizaveta Levina, Ji Zhu

Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models

Sudipto Banerjee, Xiang Chen, Ian Frankenburg, Daniel Zhou

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

Andrea Perin, Stephane Deny

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

Michal Derezinski, Daniel LeJeune, Deanna Needell, Elizaveta Rebrova

Deep Generative Models: Complexity, Dimensionality, and Approximation

Kevin Wang, Hongqian Niu, Yixin Wang, Didong Li

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

Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching

Jannis Chemseddine, Paul Hagemann, Gabriele Steidl, Christian Wald

Deep Variational Multivariate Information Bottleneck - A Framework for Variational Losses

Eslam Abdelaleem, Ilya Nemenman, K. Michael Martini

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

Romain Verdière, Clémentine Prieur, Olivier Zahm

Finite Expression Method for Solving High-Dimensional Partial Differential Equations

Senwei Liang, Haizhao Yang

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

Ziwen Wang, Yancheng Yuan, Jiaming Ma, Tieyong Zeng, Defeng Sun

Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary

Tianyang Hu, Ruiqi Liu, Zuofeng Shang, Guang Cheng

Optimal and Efficient Algorithms for Decentralized Online Convex Optimization

Yuanyu Wan, Tong Wei, Bo Xue, Mingli Song, Lijun Zhang

Characterizing Dynamical Stability of Stochastic Gradient Descent in Overparameterized Learning

Dennis Chemnitz, Maximilian Engel

PREMAP: A Unifying PREiMage APproximation Framework for Neural Networks

Xiyue Zhang, Benjie Wang, Marta Kwiatkowska, Huan Zhang

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

Céline Comte, Matthieu Jonckheere, Jaron Sanders, Albert Senen-Cerda

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

Categorical Semantics of Compositional Reinforcement Learning

Georgios Bakirtzis, Michail Savvas, Ufuk Topcu

Transformers from Diffusion: A Unified Framework for Neural Message Passing

Qitian Wu, David Wipf, Junchi Yan

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

Yong Lin, Chen Liu, Chenlu Ye, Qing LIAN, Yuan Yao

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

Noufel FRIKHA, Maximilien GERMAIN, Mathieu Lauriere, Huyen PHAM, Xuanye SONG

Modelling Populations of Interaction Networks via Distance Metrics

George Bolt, Simón Lunagómez, Christopher Nemeth

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

Hongyu Wang, Shuming Ma, Lingxiao Ma, Lei Wang, Wenhui Wang

Physics-informed Kernel Learning

Nathan Doumèche, Francis Bach, Gérard Biau, Claire Boyer

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

Yuqing Liang, Dongpo Xu

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

Paul Egels, Ismaël Castillo

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

Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi

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

Yuki Ohnishi, Bikram Karmakar, Arman Sabbaghi

Quantifying the Effectiveness of Linear Preconditioning in Markov Chain Monte Carlo

Max Hird, Samuel Livingstone

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

Penghe Zhang, Naihua Xiu, Hou-Duo Qi

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

Anish Agarwal, Devavrat Shah, Dennis Shen

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

Ben Wu, Keru Wu, Jian Kang

DRM Revisited: A Complete Error Analysis

Yuling Jiao, Ruoxuan Li, Peiying Wu, Jerry Zhijian Yang, Pingwen Zhang

Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF

Han Shen, Zhuoran Yang, Tianyi Chen

Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers

Fan Yang, Hongyang R. Zhang, Sen Wu, Christopher Re, Weijie J. Su

Score-based Causal Representation Learning: Linear and General Transformations

Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Abhishek Kumar, Ali Tajer

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

Saptarshi Chakraborty, Peter L. Bartlett

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

Keru Wu, Yuansi Chen, Wooseok Ha, Bin Yu