JMLR 2025

309 papers

Mean-Field Variational Inference via Wasserstein Gradient Flow

Rentian Yao, Yun Yang

Certified Machine Unlearning Under High Dimensional Regime

Haolin Zou, Arnab Auddy, Yongchan Kwon, Kamiar Rahnama Rad, Arian Maleki

FusionBench: A Unified Library and Comprehensive Benchmark for Deep Model Fusion

Anke Tang, Li Shen, Yong Luo, Enneng Yang, Han Hu

Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients

Sachin Garg, Albert S. Berahas, Michał Dereziński

Gradient Equilibrium in Online Learning: Theory and Applications

Anastasios N. Angelopoulos, Michael I. Jordan, Ryan J. Tibshirani

Mixing Times and Privacy Analysis for the Projected Langevin Algorithm under a Modulus of Continuity

Mario Bravo, Juan P. Flores-Mella, Cristóbal Guzmán

Neural Operators Can Play Dynamic Stackelberg Games

Guillermo A. Alvarez, Ibrahim Ekren, Anastasis Kratsios, Xuwei Yang

Reward-Directed Score-Based Diffusion Models via q-Learning

Xuefeng GAO, Jiale Zha, Xun Yu Zhou

Bayesian Score Calibration for Approximate Models

Joshua J. Bon, David J. Warne, David J. Nott, Christopher Drovandi

Continuum Attention for Neural Operators

Edoardo Calvello, Nikola B. Kovachki, Matthew E. Levine, Andrew M. Stuart

Geometry-Dependent Matching Pursuit: a Transition Phase for Convergence on Linear Regression and LASSO

Celine Moucer, Adrien B. Taylor, Francis Bach

A statistical perspective on algorithm unrolling models for inverse problems

Yves Atchade, Xinru Liu, Qiuyun Zhu

Geodesic Slice Sampling on the Sphere

Michael Habeck, Mareike Hasenpflug, Shantanu Kodgirwar, Daniel Rudolf

Kernel-based L_2-Boosting with Structure Constraints

Yao Wang, Xin Guo, Shao-Bo Lin

Global Convergence of Adjoint-Optimized Neural PDEs

Konstantin Riedl, Justin Sirignano, Konstantinos Spiliopoulos

Zono-Conformal Prediction: Zonotope-Based Uncertainty Quantification for Regression and Classification Tasks

Laura Lützow, Michael Eichelbeck, Mykel J. Kochenderfer, Matthias Althoff

Optimal Denoising in Score-Based Generative Models: The Role of Data Regularity

Eliot Beyler, Francis Bach

O(d/T) Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions

Gen Li, Yuling Yan

The Z-Gromov-Wasserstein Distance

Martin Bauer, Facundo Mémoli, Tom Needham, Mao Nishino

Equivariant Manifold Neural ODEs and Differential Invariants

Emma Andersdotter, Daniel Persson, Fredrik Ohlsson

DeepCAVE: A Visualization and Analysis Tool for Automated Machine Learning

Sarah Segel, Helena Graf, Edward Bergman, Kristina Thieme, Marcel Wever

Proper losses regret at least 1/2-order

Han Bao, Asuka Takatsu

CAP: A General Algorithm for Online Selective Conformal Prediction with FCR Control

Yajie Bao, Yuyang Huo, Haojie Ren, Changliang Zou

Client Selection for Federated Policy Optimization with Environment Heterogeneity

Zhijie Xie, Shenghui Song

Step and Smooth Decompositions as Topological Clustering

Luciano Vinas, Arash A. Amini

Nonlinear functional regression by functional deep neural network with kernel embedding

Zhongjie Shi, Jun Fan, Linhao Song, Ding-Xuan Zhou, Johan A.K. Suykens

Almost Sure Convergence of Dropout Algorithms for Neural Networks

Albert Senen--Cerda, Jaron Sanders

Geometric Operator Learning with Optimal Transport

Xinyi Li, Zongyi Li, Nikola Kovachki, Anima Anandkumar

Backward Filtering Forward Guiding

Frank H. van der Meulen, S. Sommer

Optimal Decentralized Composite Optimization for Strongly Convex Functions

Haishan Ye, Xiangyu Chang

Bias-variance decompositions: the exclusive privilege of Bregman divergences

Tom Heskes

Concentration of Cumulative Reward in Markov Decision Processes

Borna Sayedana, Peter E. Caines, Aditya Mahajan

Combining Climate Models using Bayesian Regression Trees and Random Paths

John C. Yannotty, Thomas J. Santner, Bo Li, Matthew T. Pratola

The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs

Peter Mostowsky, Vincent Dutordoir, Iskander Azangulov, Noémie Jaquier, Michael John Hutchinson

Physics-Informed Deep Learning and Compressive Collocation for High-Dimensional Diffusion-Reaction Equations: Practical Existence Theory and Numerics

Simone Brugiapaglia, Nick Dexter, Samir Karam, Weiqi Wang

Substitute Adjustment via Recovery of Latent Variables

Jeffrey Adams, Niels Richard Hansen

Good regularity creates large learning rate implicit biases: edge of stability, balancing, and catapult

Yuqing Wang, Zhenghao Xu, Tuo Zhao, Molei Tao

FedHB: Hierarchical Bayesian Federated Learning

Minyoung Kim, Timothy Hospedales

Sketching in High-Dimensional Regression With Big Data Using Gaussian Scale Mixture Priors

Rajarshi Guhaniyogi, Aaron Wolfe Scheffler

Optimal Functional Bilinear Regression with Two-dimensional Functional Covariates via Reproducing Kernel Hilbert Space

Dan Yang, Jianlong Shao, Haipeng Shen, Hongtu Zhu

Towards Understanding Gradient Flow Dynamics of Homogeneous Neural Networks Beyond the Origin

Akshay Kumar, Jarvis Haupt

Optimal Complexity in Byzantine-Robust Distributed Stochastic Optimization with Data Heterogeneity

Qiankun Shi, Jie Peng, Kun Yuan, Xiao Wang, Qing Ling

Towards Unified Native Spaces in Kernel Methods

Xavier Emery, Emilio Porcu, Moreno Bevilacqua

TorchCP: A Python Library for Conformal Prediction

Jianguo Huang, Jianqing Song, Xuanning Zhou, Bingyi Jing, Hongxin Wei

Hopfield-Fenchel-Young Networks: A Unified Framework for Associative Memory Retrieval

Saul Santos, Vlad Niculae, Daniel McNamee, André F.T. Martins

Identifiability of Causal Graphs under Non-Additive Conditionally Parametric Causal Models

Juraj Bodik, Valérie Chavez-Demoulin

Fundamental Limits of Membership Inference Attacks on Machine Learning Models

Eric Aubinais, Elisabeth Gassiat, Pablo Piantanida

On the Robustness of Kernel Goodness-of-Fit Tests

Xing Liu, François-Xavier Briol

Efficient Online Prediction for High-Dimensional Time Series via Joint Tensor Tucker Decomposition

Zhenting Luan, Defeng Sun, Haoning Wang, Liping Zhang

Fast Computation of Superquantile-Constrained Optimization Through Implicit Scenario Reduction

Jake Roth, Ying Cui

Collaborative likelihood-ratio estimation over graphs

Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos

On the Utility of Equal Batch Sizes for Inference in Stochastic Gradient Descent

Rahul Singh, Abhinek Shukla, Dootika Vats

Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies

Zhanyu Wang, Guang Cheng, Jordan Awan

Convergence and Sample Complexity of Natural Policy Gradient Primal-Dual Methods for Constrained MDPs

Dongsheng Ding, Kaiqing Zhang, Jiali Duan, Tamer Basar, Mihailo R. Jovanovic

Differentially Private Multivariate Medians

Kelly Ramsay, Aukosh Jagannath, Shoja'eddin Chenouri

VFOSA: Variance-Reduced Fast Operator Splitting Algorithms for Generalized Equations

Quoc Tran-Dinh

Scaling Capability in Token Space: An Analysis of Large Vision Language Model

Tenghui Li, Guoxu Zhou, Xuyang Zhao, Qibin Zhao

Minimax Optimal Two-Sample Testing under Local Differential Privacy

Jongmin Mun, Seungwoo Kwak, Ilmun Kim

Jackpot: Approximating Uncertainty Domains with Adversarial Manifolds

Nathanaël Munier, Emmanuel Soubies, Pierre Weiss

An Asymptotically Optimal Coordinate Descent Algorithm for Learning Bayesian Networks from Gaussian Models

Tong Xu, Simge Kucukyavuz, Ali Shojaie, Armeen Taeb

Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation

David Holzmüller, Francis Bach

A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning

Samuel E. Otto, Nicholas Zolman, J. Nathan Kutz, Steven L. Brunton

Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection

Nikita Zozoulenko, Thomas Cass, Lukas Gonon

Stable learning using spiking neural networks equipped with affine encoders and decoders

A. Martina Neuman, Dominik Dold, Philipp Christian Petersen

Efficient Knowledge Deletion from Trained Models Through Layer-wise Partial Machine Unlearning

Vinay Chakravarthi Gogineni, Esmaeil S. Nadimi

General Loss Functions Lead to (Approximate) Interpolation in High Dimensions

Kuo-Wei Lai, Vidya Muthukumar

Piecewise deterministic sampling with splitting schemes

Andrea Bertazzi, Paul Dobson, Pierre Monmarché

Hierarchical and Stochastic Crystallization Learning: Geometrically Leveraged Nonparametric Regression with Delaunay Triangulation

Jiaqi Gu, Guosheng Yin

Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2

Yuri Chervonyi, Trieu H. Trinh, Miroslav Olšák, Xiaomeng Yang, Hoang H. Nguyen

Decentralized Bilevel Optimization: A Perspective from Transient Iteration Complexity

Boao Kong, Shuchen Zhu, Songtao Lu, Xinmeng Huang, Kun Yuan

Fair Text Classification via Transferable Representations

Thibaud Leteno, Michaël Perrot, Charlotte Laclau, Antoine Gourru, Christophe Gravier

Stochastic Interior-Point Methods for Smooth Conic Optimization with Applications

Chuan He, Zhanwang Deng

Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration

Tao Sun, Xinwang Liu, Kun Yuan

Generalized multi-view model: Adaptive density estimation under low-rank constraints

Julien Chhor, Olga Klopp, Alexandre B. Tsybakov

(De)-regularized Maximum Mean Discrepancy Gradient Flow

Zonghao Chen, Aratrika Mustafi, Pierre Glaser, Anna Korba, Arthur Gretton

On Probabilistic Embeddings in Optimal Dimension Reduction

Ryan Murray, Adam Pickarski

Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficient-KAN and WAV-KAN

Subhajit Patra, Sonali Panda, Bikram Keshari Parida, Mahima Arya, Kurt Jacobs

Graph-accelerated Markov Chain Monte Carlo using Approximate Samples

Leo L. Duan, Anirban Bhattacharya

Online Quantile Regression

Yinan Shen, Dong Xia, Wen-Xin Zhou

Statistical Inference of Random Graphs With a Surrogate Likelihood Function

Dingbo Wu, Fangzheng Xie

On the Representation of Pairwise Causal Background Knowledge and Its Applications in Causal Inference

Zhuangyan Fang, Ruiqi Zhao, Yue Liu, Yangbo He

An Augmentation Overlap Theory of Contrastive Learning

Qi Zhang, Yifei Wang, Yisen Wang

Algorithms for ridge estimation with convergence guarantees

Wanli Qiao, Wolfgang Polonik

Talent: A Tabular Analytics and Learning Toolbox

Si-Yang Liu, Hao-Run Cai, Qi-Le Zhou, Huai-Hong Yin, Tao Zhou

Inferring Change Points in High-Dimensional Regression via Approximate Message Passing

Gabriel Arpino, Xiaoqi Liu, Julia Gontarek, Ramji Venkataramanan

Universality of Kernel Random Matrices and Kernel Regression in the Quadratic Regime

Parthe Pandit, Zhichao Wang, Yizhe Zhu

Lexicographic Lipschitz Bandits: New Algorithms and a Lower Bound

Bo Xue, Ji Cheng, Fei Liu, Yimu Wang, Lijun Zhang

On the Natural Gradient of the Evidence Lower Bound

Nihat Ay, Jesse van Oostrum, Adwait Datar

Geometry and Stability of Supervised Learning Problems

Facundo Mémoli, Brantley Vose, Robert C. Williamson

Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination

Peng Wang, Xiao Li, Can Yaras, Zhihui Zhu, Laura Balzano

Optimal Rates of Kernel Ridge Regression under Source Condition in Large Dimensions

Haobo Zhang, Yicheng Li, Weihao Lu, Qian Lin

A Hybrid Weighted Nearest Neighbour Classifier for Semi-Supervised Learning

Stephen M. S. Lee, Mehdi Soleymani

Scalable and Adaptive Variational Bayes Methods for Hawkes Processes

Déborah Sulem, Vincent Rivoirard, Judith Rousseau

Biological Sequence Kernels with Guaranteed Flexibility

Alan N. Amin, Debora S. Marks, Eli N. Weinstein

Unified Discrete Diffusion for Categorical Data

Lingxiao Zhao, Xueying Ding, Lijun Yu, Leman Akoglu

Reinforcement Learning for Infinite-Dimensional Systems

Wei Zhang, Jr-Shin Li

Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation

Hao Liu, Jiahui Cheng, Wenjing Liao

Generation of Geodesics with Actor-Critic Reinforcement Learning to Predict Midpoints

Kazumi Kasaura

Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation

Michael Sucker, Jalal Fadili, Peter Ochs

Sparse Semiparametric Discriminant Analysis for High-dimensional Zero-inflated Data

Hee Cheol Chung, Yang Ni, Irina Gaynanova