JMLR 2024

AMLB: An AutoML Benchmark Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl, Joaquin Vanschoren

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An Algorithm with Optimal Dimension-Dependence for Zero-Order Nonsmooth Nonconvex Stochastic Optimization Guy Kornowski, Ohad Shamir

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An Algorithmic Framework for the Optimization of Deep Neural Networks Architectures and Hyperparameters Julie Keisler, El-Ghazali Talbi, Sandra Claudel, Gilles Cabriel

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An Analysis of Quantile Temporal-Difference Learning Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney

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An Asymptotic Study of Discriminant and Vote-Averaging Schemes for Randomly-Projected Linear Discriminants Lama B. Niyazi, Abla Kammoun, Hayssam Dahrouj, Mohamed-Slim Alouini, Tareq Y. Al-Naffouri

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An Embedding Framework for the Design and Analysis of Consistent Polyhedral Surrogates Jessie Finocchiaro, Rafael M. Frongillo, Bo Waggoner

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An Entropy-Based Model for Hierarchical Learning Amir R. Asadi

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An Inexact Projected Regularized Newton Method for Fused Zero-Norms Regularization Problems Yuqia Wu, Shaohua Pan, Xiaoqi Yang

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An Optimal Transport Approach for Computing Adversarial Training Lower Bounds in Multiclass Classification Nicolas Garcia Trillos, Matt Jacobs, Jakwang Kim, Matthew Werenski

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Approximate Bayesian Inference from Noisy Likelihoods with Gaussian Process Emulated MCMC Marko Järvenpää, Jukka Corander

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Approximate Information Tests on Statistical Submanifolds Michael W. Trosset, Carey E. Priebe

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Assessing the Overall and Partial Causal Well-Specification of Nonlinear Additive Noise Models Christoph Schultheiss, Peter Bühlmann

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Axiomatic Effect Propagation in Structural Causal Models Raghav Singal, George Michailidis

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Bagging Provides Assumption-Free Stability Jake A. Soloff, Rina Foygel Barber, Rebecca Willett

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Bayesian Regression Markets Thomas Falconer, Jalal Kazempour, Pierre Pinson

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Bayesian Structural Learning with Parametric Marginals for Count Data: An Application to Microbiota Systems Veronica Vinciotti, Pariya Behrouzi, Reza Mohammadi

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Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box Ryan Giordano, Martin Ingram, Tamara Broderick

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Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria Tengyuan Liang

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Boundary Constrained Gaussian Processes for Robust Physics-Informed Machine Learning of Linear Partial Differential Equations David Dalton, Alan Lazarus, Hao Gao, Dirk Husmeier

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Bridging Distributional and Risk-Sensitive Reinforcement Learning with Provable Regret Bounds Hao Liang, Zhi-Quan Luo

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Causal Discovery with Generalized Linear Models Through Peeling Algorithms Minjie Wang, Xiaotong Shen, Wei Pan

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Causal Effects of Intervening Variables in Settings with Unmeasured Confounding Lan Wen, Aaron Sarvet, Mats Stensrud

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Characterization of Translation Invariant MMD on Rd and Connections with Wasserstein Distances Thibault Modeste, Clément Dombry

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Choosing the Number of Topics in LDA Models – A Monte Carlo Comparison of Selection Criteria Victor Bystrov, Viktoriia Naboka-Krell, Anna Staszewska-Bystrova, Peter Winker

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Classification of Data Generated by Gaussian Mixture Models Using Deep ReLU Networks Tian-Yi Zhou, Xiaoming Huo

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Classification with Deep Neural Networks and Logistic Loss Zihan Zhang, Lei Shi, Ding-Xuan Zhou

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Cluster-Adaptive Network A/B Testing: From Randomization to Estimation Yang Liu, Yifan Zhou, Ping Li, Feifang Hu

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Commutative Scaling of Width and Depth in Deep Neural Networks Soufiane Hayou

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Compressed and Distributed Least-Squares Regression: Convergence Rates with Applications to Federated Learning Constantin Philippenko, Aymeric Dieuleveut

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Concentration and Moment Inequalities for General Functions of Independent Random Variables with Heavy Tails Shaojie Li, Yong Liu

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Conformal Inference for Online Prediction with Arbitrary Distribution Shifts Isaac Gibbs, Emmanuel J. Candès

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Consistent Multiclass Algorithms for Complex Metrics and Constraints Harikrishna Narasimhan, Harish G. Ramaswamy, Shiv Kumar Tavker, Drona Khurana, Praneeth Netrapalli, Shivani Agarwal

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Contamination-Source Based K-Sample Clustering Xavier Milhaud, Denys Pommeret, Yahia Salhi, Pierre Vandekerkhove

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Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression Aleksandrs Slivkins, Xingyu Zhou, Karthik Abinav Sankararaman, Dylan J. Foster

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Continuous Prediction with Experts' Advice Nicholas J. A. Harvey, Christopher Liaw, Victor S. Portella

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Convergence for Nonconvex ADMM, with Applications to CT Imaging Rina Foygel Barber, Emil Y. Sidky

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Convergence of Message-Passing Graph Neural Networks with Generic Aggregation on Large Random Graphs Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter

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Correction to "Wasserstein Distance Estimates for the Distributions of Numerical Approximations to Ergodic Stochastic Differential Equations" Daniel Paulin, Peter A. Whalley

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Countering the Communication Bottleneck in Federated Learning: A Highly Efficient Zero-Order Optimization Technique Elissa Mhanna, Mohamad Assaad

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Critically Assessing the State of the Art in Neural Network Verification Matthias König, Annelot W. Bosman, Holger H. Hoos, Jan N. van Rijn

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DAG-Informed Structure Learning from Multi-Dimensional Point Processes Chunming Zhang, Muhong Gao, Shengji Jia

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Data Summarization via Bilevel Optimization Zalán Borsos, Mojmír Mutný, Marco Tagliasacchi, Andreas Krause

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Data Thinning for Convolution-Closed Distributions Anna Neufeld, Ameer Dharamshi, Lucy L. Gao, Daniela Witten

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Data-Driven Automated Negative Control Estimation (DANCE): Search for, Validation of, and Causal Inference with Negative Controls Erich Kummerfeld, Jaewon Lim, Xu Shi

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Data-Efficient Policy Evaluation Through Behavior Policy Search Josiah P. Hanna, Yash Chandak, Philip S. Thomas, Martha White, Peter Stone, Scott Niekum

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Debiasing Evaluations That Are Biased by Evaluations Jingyan Wang, Ivan Stelmakh, Yuting Wei, Nihar Shah

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Decentralized Natural Policy Gradient with Variance Reduction for Collaborative Multi-Agent Reinforcement Learning Jinchi Chen, Jie Feng, Weiguo Gao, Ke Wei

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Decomposed Linear Dynamical Systems (dLDS) for Learning the Latent Components of Neural Dynamics Noga Mudrik, Yenho Chen, Eva Yezerets, Christopher J. Rozell, Adam S. Charles

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Decomposing Global Feature Effects Based on Feature Interactions Julia Herbinger, Marvin N. Wright, Thomas Nagler, Bernd Bischl, Giuseppe Casalicchio

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Decorrelated Variable Importance Isabella Verdinelli, Larry Wasserman

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Deep Backward and Galerkin Methods for the Finite State Master Equation Asaf Cohen, Mathieu Laurière, Ethan Zell

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Deep Network Approximation: Beyond ReLU to Diverse Activation Functions Shijun Zhang, Jianfeng Lu, Hongkai Zhao

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Deep Neural Network Approximation of Invariant Functions Through Dynamical Systems Qianxiao Li, Ting Lin, Zuowei Shen

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Deep Nonparametric Estimation of Operators Between Infinite Dimensional Spaces Hao Liu, Haizhao Yang, Minshuo Chen, Tuo Zhao, Wenjing Liao

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Deep Nonparametric Quantile Regression Under Covariate Shift Xingdong Feng, Xin He, Yuling Jiao, Lican Kang, Caixing Wang

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Depth Degeneracy in Neural Networks: Vanishing Angles in Fully Connected ReLU Networks on Initialization Cameron Jakub, Mihai Nica

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Desiderata for Representation Learning: A Causal Perspective Yixin Wang, Michael I. Jordan

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Differentially Private Data Release for Mixed-Type Data via Latent Factor Models Yanqing Zhang, Qi Xu, Niansheng Tang, Annie Qu

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Differentially Private Methods for Managing Model Uncertainty in Linear Regression Víctor Peña, Andrés F. Barrientos

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Differentially Private Topological Data Analysis Taegyu Kang, Sehwan Kim, Jinwon Sohn, Jordan Awan

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Distributed Estimation on Semi-Supervised Generalized Linear Model Jiyuan Tu, Weidong Liu, Xiaojun Mao

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Distributed Gaussian Mean Estimation Under Communication Constraints: Optimal Rates and Communication-Efficient Algorithms T. Tony Cai, Hongji Wei

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Distributed Kernel-Driven Data Clustering Ioannis Schizas

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Distribution Learning via Neural Differential Equations: A Nonparametric Statistical Perspective Youssef Marzouk, Zhi Ren, Sven Wang, Jakob Zech

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Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity Laixi Shi, Yuejie Chi

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Dropout Regularization Versus L2-Penalization in the Linear Model Gabriel Clara, Sophie Langer, Johannes Schmidt-Hieber

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Effect-Invariant Mechanisms for Policy Generalization Sorawit Saengkyongam, Niklas Pfister, Predrag Klasnja, Susan Murphy, Jonas Peters

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Efficient Active Manifold Identification via Accelerated Iteratively Reweighted Nuclear Norm Minimization Hao Wang, Ye Wang, Xiangyu Yang

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Efficient Convex Algorithms for Universal Kernel Learning Aleksandr Talitckii, Brendon Colbert, Matthew M. Peet

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Efficient Modality Selection in Multimodal Learning Yifei He, Runxiang Cheng, Gargi Balasubramaniam, Yao-Hung Hubert Tsai, Han Zhao

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Empirical Design in Reinforcement Learning Andrew Patterson, Samuel Neumann, Martha White, Adam White

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ENNS: Variable Selection, Regression, Classification, and Deep Neural Network for High-Dimensional Data Kaixu Yang, Arkaprabha Ganguli, Tapabrata Maiti

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Entropic Gromov-Wasserstein Distances: Stability and Algorithms Gabriel Rioux, Ziv Goldfeld, Kengo Kato

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Estimating the Minimizer and the Minimum Value of a Regression Function Under Passive Design Arya Akhavan, Davit Gogolashvili, Alexandre B. Tsybakov

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Estimating the Replication Probability of Significant Classification Benchmark Experiments Daniel Berrar

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Estimation of Sparse Gaussian Graphical Models with Hidden Clustering Structure Meixia Lin, Defeng Sun, Kim-Chuan Toh, Chengjing Wang

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Estimation of the Order of Non-Parametric Hidden Markov Models Using the Singular Values of an Integral Operator Marie Du Roy de Chaumaray, Salima El Kolei, Marie-Pierre Etienne, Matthieu Marbac

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Euler Characteristic Tools for Topological Data Analysis Olympio Hacquard, Vadim Lebovici

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Evidence Estimation in Gaussian Graphical Models Using a Telescoping Block Decomposition of the Precision Matrix Anindya Bhadra, Ksheera Sagar, David Rowe, Sayantan Banerjee, Jyotishka Datta

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Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification Natalie S. Frank, Jonathan Niles-Weed

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Exploration of the Search Space of Gaussian Graphical Models for Paired Data Alberto Roverato, Dung Ngoc Nguyen

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Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment Zixian Yang, Xin Liu, Lei Ying

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Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition Varun Kanade, Patrick Rebeschini, Tomas Vaskevicius

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Fairness Guarantees in Multi-Class Classification with Demographic Parity Christophe Denis, Romuald Elie, Mohamed Hebiri, François Hu

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Fairness in Survival Analysis with Distributionally Robust Optimization Shu Hu, George H. Chen

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False Discovery Proportion Envelopes with M-Consistency Meah Iqraa, Blanchard Gilles, Roquain Etienne

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Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization Shicong Cen, Yuting Wei, Yuejie Chi

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Fast Rates in Pool-Based Batch Active Learning Claudio Gentile, Zhilei Wang, Tong Zhang

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Faster Randomized Methods for Orthogonality Constrained Problems Boris Shustin, Haim Avron

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Faster Rates of Differentially Private Stochastic Convex Optimization Jinyan Su, Lijie Hu, Di Wang

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Fat-Shattering Dimension of K-Fold Aggregations Idan Attias, Aryeh Kontorovich

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FedCBO: Reaching Group Consensus in Clustered Federated Learning Through Consensus-Based Optimization José A. Carrillo, Nicolás García Trillos, Sixu Li, Yuhua Zhu

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Federated Automatic Differentiation Keith Rush, Zachary Charles, Zachary Garrett

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Fermat Distances: Metric Approximation, Spectral Convergence, and Clustering Algorithms Nicolás García Trillos, Anna Little, Daniel McKenzie, James M. Murphy

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FineMorphs: Affine-Diffeomorphic Sequences for Regression Michele Lohr, Laurent Younes

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Finite-Time Analysis of Globally Nonstationary Multi-Armed Bandits Junpei Komiyama, Edouard Fouché, Junya Honda

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Fisher Information Dissipation for Time-Inhomogeneous Stochastic Differential Equations Qi Feng, Xinzhe Zuo, Wuchen Li

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Fixed Points of Nonnegative Neural Networks Tomasz J. Piotrowski, Renato L. G. Cavalcante, Mateusz Gabor

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Flexible Bayesian Product Mixture Models for Vector Autoregressions Suprateek Kundu, Joshua Lukemire

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Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling Qidong Yang, Alex Hernandez-Garcia, Paula Harder, Venkatesh Ramesh, Prasanna Sattigeri, Daniela Szwarcman, Campbell D. Watson, David Rolnick

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Fréchet Random Forests for Metric Space Valued Regression with Non Euclidean Predictors Louis Capitaine, Jérémie Bigot, Rodolphe Thiébaut, Robin Genuer

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From Continuous-Time Formulations to Discretization Schemes: Tensor Trains and Robust Regression for BSDEs and Parabolic PDEs Lorenz Richter, Leon Sallandt, Nikolas Nüsken

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From Small Scales to Large Scales: Distance-to-Measure Density Based Geometric Analysis of Complex Data Katharina Proksch, Christoph Alexander Weikamp, Thomas Staudt, Benoit Lelandais, Christophe Zimmer

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Functional Directed Acyclic Graphs Kuang-Yao Lee, Lexin Li, Bing Li

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Functional Optimal Transport: Regularized mAP Estimation and Domain Adaptation for Functional Data Jiacheng Zhu, Aritra Guha, Dat Do, Mengdi Xu, XuanLong Nguyen, Ding Zhao

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Functions with Average Smoothness: Structure, Algorithms, and Learning Yair Ashlagi, Lee-Ad Gottlieb, Aryeh Kontorovich

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Gaussian Interpolation Flows Yuan Gao, Jian Huang, and Yuling Jiao

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Gaussian Mixture Models with Rare Events Xuetong Li, Jing Zhou, Hansheng Wang

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Generalization and Stability of Interpolating Neural Networks with Minimal Width Hossein Taheri, Christos Thrampoulidis

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Generalization on the Unseen, Logic Reasoning and Degree Curriculum Emmanuel Abbe, Samy Bengio, Aryo Lotfi, Kevin Rizk

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Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang

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Generative Adversarial Ranking Nets Yinghua Yao, Yuangang Pan, Jing Li, Ivor W. Tsang, Xin Yao

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Geometric Learning with Positively Decomposable Kernels Nathael Da Costa, Cyrus Mostajeran, Juan-Pablo Ortega, Salem Said

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GGD: Grafting Gradient Descent Yanjing Feng, Yongdao Zhou

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Goal-Space Planning with Subgoal Models Chunlok Lo, Kevin Roice, Parham Mohammad Panahi, Scott M. Jordan, Adam White, Gabor Mihucz, Farzane Aminmansour, Martha White

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Gradient-Free Optimization of Highly Smooth Functions: Improved Analysis and a New Algorithm Arya Akhavan, Evgenii Chzhen, Massimiliano Pontil, Alexandre B. Tsybakov

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Gradual Domain Adaptation: Theory and Algorithms Yifei He, Haoxiang Wang, Bo Li, Han Zhao

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Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length Katerina Hlaváčková-Schindler, Anna Melnykova, Irene Tubikanec

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Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data Arhit Chakrabarti, Yang Ni, Ellen Ruth A. Morris, Michael L. Salinas, Robert S. Chapkin, Bani K. Mallick

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Grokking Phase Transitions in Learning Local Rules with Gradient Descent Bojan Žunkovič, Enej Ilievski

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Guaranteed Nonconvex Factorization Approach for Tensor Train Recovery Zhen Qin, Michael B. Wakin, Zhihui Zhu

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Hamiltonian Monte Carlo for Efficient Gaussian Sampling: Long and Random Steps Simon Apers, Sander Gribling, Dániel Szilágyi

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Heterogeneity-Aware Clustered Distributed Learning for Multi-Source Data Analysis Yuanxing Chen, Qingzhao Zhang, Shuangge Ma, Kuangnan Fang

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Heterogeneous-Agent Reinforcement Learning Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng, Siyi Hu, Jiaming Ji, Yaodong Yang

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High Probability and Risk-Averse Guarantees for a Stochastic Accelerated Primal-Dual Method Yassine Laguel, Necdet Serhat Aybat, Mert Gürbüzbalaban

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High Probability Convergence Bounds for Non-Convex Stochastic Gradient Descent with Sub-Weibull Noise Liam Madden, Emiliano Dall'Anese, Stephen Becker

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Homeomorphic Projection to Ensure Neural-Network Solution Feasibility for Constrained Optimization Enming Liang, Minghua Chen, Steven H. Low

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How Two-Layer Neural Networks Learn, One (Giant) Step at a Time Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce, Ludovic Stephan

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Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations Yuanyuan Wang, Wei Huang, Mingming Gong, Xi Geng, Tongliang Liu, Kun Zhang, Dacheng Tao

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Identifying Causal Eﬀects Using Instrumental Time Series: Nuisance IV and Correcting for the past Nikolaj Thams, Rikke Søndergaard, Sebastian Weichwald, Jonas Peters

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Improved Random Features for Dot Product Kernels Jonas Wacker, Motonobu Kanagawa, Maurizio Filippone

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Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions Stanislas Ducotterd, Alexis Goujon, Pakshal Bohra, Dimitris Perdios, Sebastian Neumayer, Michael Unser

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Improving Physics-Informed Neural Networks with Meta-Learned Optimization Alex Bihlo

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Individual-Centered Partial Information in Social Networks Xiao Han, Y. X. Rachel Wang, Qing Yang, Xin Tong

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Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization Under Orthogonality Constraints Pierre Ablin, Simon Vary, Bin Gao, Pierre-Antoine Absil

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Inference on High-Dimensional Single-Index Models with Streaming Data Dongxiao Han, Jinhan Xie, Jin Liu, Liuquan Sun, Jian Huang, Bei Jiang, Linglong Kong

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Infinite-Dimensional Diffusion Models Jakiw Pidstrigach, Youssef Marzouk, Sebastian Reich, Sven Wang

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Information Capacity Regret Bounds for Bandits with Mediator Feedback Khaled Eldowa, Nicolò Cesa-Bianchi, Alberto Maria Metelli, Marcello Restelli

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Information Processing Equalities and the Information–Risk Bridge Robert C. Williamson, Zac Cranko

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Information-Theoretic Generalization Bounds for Transductive Learning and Its Applications Huayi Tang, Yong Liu

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Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning Luofeng Liao, Zuyue Fu, Zhuoran Yang, Yixin Wang, Dingli Ma, Mladen Kolar, Zhaoran Wang

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Interpretable Algorithmic Fairness in Structured and Unstructured Data Hari Bandi, Dimitris Bertsimas, Thodoris Koukouvinos, Sofie Kupiec

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Invariant Physics-Informed Neural Networks for Ordinary Differential Equations Shivam Arora, Alex Bihlo, Francis Valiquette

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Iterate Averaging in the Quest for Best Test Error Diego Granziol, Nicholas P. Baskerville, Xingchen Wan, Samuel Albanie, Stephen Roberts

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Just Wing It: Near-Optimal Estimation of Missing Mass in a Markovian Sequence Ashwin Pananjady, Vidya Muthukumar, Andrew Thangaraj

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Kernel Thinning Raaz Dwivedi, Lester Mackey

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Label Alignment Regularization for Distribution Shift Ehsan Imani, Guojun Zhang, Runjia Li, Jun Luo, Pascal Poupart, Philip H.S. Torr, Yangchen Pan

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Label Noise Robustness of Conformal Prediction Bat-Sheva Einbinder, Shai Feldman, Stephen Bates, Anastasios N. Angelopoulos, Asaf Gendler, Yaniv Romano

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Law of Large Numbers and Central Limit Theorem for Wide Two-Layer Neural Networks: The Mini-Batch and Noisy Case Arnaud Descours, Arnaud Guillin, Manon Michel, Boris Nectoux

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Learnability of Linear Port-Hamiltonian Systems Juan-Pablo Ortega, Daiying Yin

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Learning and Scoring Gaussian Latent Variable Causal Models with Unknown Additive Interventions Armeen Taeb, Juan L. Gamella, Christina Heinze-Deml, Peter Bühlmann

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Learning Discretized Neural Networks Under Ricci Flow Jun Chen, Hanwen Chen, Mengmeng Wang, Guang Dai, Ivor W. Tsang, Yong Liu

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Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach Shuang Qiu, Boxiang Lyu, Qinglin Meng, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan

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Learning from Many Trajectories Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi

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Learning Gaussian DAGs from Network Data Hangjian Li, Oscar Hernan Madrid Padilla, Qing Zhou

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Learning Non-Gaussian Graphical Models via Hessian Scores and Triangular Transport Ricardo Baptista, Rebecca Morrison, Olivier Zahm, Youssef Marzouk

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Learning Optimal Dynamic Treatment Regimens Subject to Stagewise Risk Controls Mochuan Liu, Yuanjia Wang, Haoda Fu, Donglin Zeng

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Learning Regularized Graphon Mean-Field Games with Unknown Graphons Fengzhuo Zhang, Vincent Y. F. Tan, Zhaoran Wang, Zhuoran Yang

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Learning to Warm-Start Fixed-Point Optimization Algorithms Rajiv Sambharya, Georgina Hall, Brandon Amos, Bartolomeo Stellato

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Learning with a Linear Loss Function: Excess Risk and Estimation Bounds for ERM, Minmax MOM and Their Regularized Versions with Applications to Robustness in Sparse PCA. Guillaume Lecué, Lucie Neirac

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Learning with Norm Constrained, Over-Parameterized, Two-Layer Neural Networks Fanghui Liu, Leello Dadi, Volkan Cevher

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Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation Learning Jaesun Shin, Eunjoo Jeon, Taewon Cho, Namkyeong Cho, Youngjune Gwon

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Linear Distance Metric Learning with Noisy Labels Meysam Alishahi, Anna Little, Jeff M. Phillips

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Linear Regression with Unmatched Data: A Deconvolution Perspective Mona Azadkia, Fadoua Balabdaoui

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Localisation of Regularised and Multiview Support Vector Machine Learning Aurelian Gheondea, Cankat Tilki

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Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond Nathan Kallus, Xiaojie Mao, Masatoshi Uehara

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Log Barriers for Safe Black-Box Optimization with Application to Safe Reinforcement Learning Ilnura Usmanova, Yarden As, Maryam Kamgarpour, Andreas Krause

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Logistic Regression Under Network Dependence Somabha Mukherjee, Ziang Niu, Sagnik Halder, Bhaswar B. Bhattacharya, George Michailidis

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Low-Rank Matrix Estimation in the Presence of Change-Points Lei Shi, Guanghui Wang, Changliang Zou

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Low-Rank Variational Bayes Correction to the Laplace Method Janet van Niekerk, Haavard Rue

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Lower Bounds on the Bayesian Risk via Information Measures Amedeo Roberto Esposito, Adrien Vandenbroucque, Michael Gastpar

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Lower Complexity Adaptation for Empirical Entropic Optimal Transport Michel Groppe, Shayan Hundrieser

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Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction Yuze Han, Guangzeng Xie, Zhihua Zhang

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Manifold Learning by Mixture Models of VAEs for Inverse Problems Giovanni S. Alberti, Johannes Hertrich, Matteo Santacesaria, Silvia Sciutto

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MAP- and MLE-Based Teaching Hans Ulrich Simon, Jan Arne Telle

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Margin-Based Active Learning of Classifiers Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice

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Materials Discovery Using Max K-Armed Bandit Nobuaki Kikkawa, Hiroshi Ohno

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Mathematical Framework for Online Social Media Auditing Wasim Huleihel, Yehonathan Refael

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Matryoshka Policy Gradient for Entropy-Regularized RL: Convergence and Global Optimality François G. Ged, Maria Han Veiga

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Mean-Field Approximation of Cooperative Constrained Multi-Agent Reinforcement Learning (CMARL) Washim Uddin Mondal, Vaneet Aggarwal, Satish V. Ukkusuri

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Mean-Field Games with Finitely Many Players: Independent Learning and Subjectivity Bora Yongacoglu, Gürdal Arslan, Serdar Yüksel

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Mean-Square Analysis of Discretized Itô Diffusions for Heavy-Tailed Sampling Ye He, Tyler Farghly, Krishnakumar Balasubramanian, Murat A. Erdogdu

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Measuring Sample Quality in Algorithms for Intractable Normalizing Function Problems Bokgyeong Kang, John Hughes, Murali Haran

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Memorization with Neural Nets: Going Beyond the Worst Case Sjoerd Dirksen, Patrick Finke, Martin Genzel

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Memory of Recurrent Networks: Do We Compute It Right? Giovanni Ballarin, Lyudmila Grigoryeva, Juan-Pablo Ortega

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Memory-Efficient Sequential Pattern Mining with Hybrid Tries Amin Hosseininasab, Willem-Jan van Hoeve, Andre A. Cire

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Mentored Learning: Improving Generalization and Convergence of Student Learner Xiaofeng Cao, Yaming Guo, Heng Tao Shen, Ivor W. Tsang, James T. Kwok

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Minimax Rates for High-Dimensional Random Tessellation Forests Eliza O'Reilly, Ngoc Mai Tran

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MLRegTest: A Benchmark for the Machine Learning of Regular Languages Sam van der Poel, Dakotah Lambert, Kalina Kostyszyn, Tiantian Gao, Rahul Verma, Derek Andersen, Joanne Chau, Emily Peterson, Cody St. Clair, Paul Fodor, Chihiro Shibata, Jeffrey Heinz

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Model-Free Representation Learning and Exploration in Low-Rank MDPs Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal

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Modeling Random Networks with Heterogeneous Reciprocity Daniel Cirkovic, Tiandong Wang

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Monotonic Risk Relationships Under Distribution Shifts for Regularized Risk Minimization Daniel LeJeune, Jiayu Liu, Reinhard Heckel

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More Efficient Estimation of Multivariate Additive Models Based on Tensor Decomposition and Penalization Xu Liu, Heng Lian, Jian Huang

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More PAC-Bayes Bounds: From Bounded Losses, to Losses with General Tail Behaviors, to Anytime Validity Borja Rodríguez-Gálvez, Ragnar Thobaben, Mikael Skoglund

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Multi-Class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini

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Multi-Objective Neural Architecture Search by Learning Search Space Partitions Yiyang Zhao, Linnan Wang, Tian Guo

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Multi-Response Linear Discriminant Analysis in High Dimensions Kai Deng, Xin Zhang, Aaron J. Molstad

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Multiple Descent in the Multiple Random Feature Model Xuran Meng, Jianfeng Yao, Yuan Cao

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Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization Lesi Chen, Luo Luo

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Nearest Neighbor Sampling for Covariate Shift Adaptation François Portier, Lionel Truquet, Ikko Yamane

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Neural Bayes Estimators for Censored Inference with Peaks-over-Threshold Models Jordan Richards, Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Raphaël Huser

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Neural Collapse for Unconstrained Feature Model Under Cross-Entropy Loss with Imbalanced Data Wanli Hong, Shuyang Ling

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Neural Feature Learning in Function Space Xiangxiang Xu, Lizhong Zheng

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Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space Zhengdao Chen

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Neural Networks with Sparse Activation Induced by Large Bias: Tighter Analysis with Bias-Generalized NTK Hongru Yang, Ziyu Jiang, Ruizhe Zhang, Yingbin Liang, Zhangyang Wang

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Non-Euclidean Monotone Operator Theory and Applications Alexander Davydov, Saber Jafarpour, Anton V. Proskurnikov, Francesco Bullo

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Non-Splitting Neyman-Pearson Classifiers Jingming Wang, Lucy Xia, Zhigang Bao, Xin Tong

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Nonasymptotic Analysis of Stochastic Gradient Hamiltonian Monte Carlo Under Local Conditions for Nonconvex Optimization O. Deniz Akyildiz, Sotirios Sabanis

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Nonparametric Copula Models for Multivariate, Mixed, and Missing Data Joseph Feldman, Daniel R. Kowal

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Nonparametric Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks Guohao Shen, Yuling Jiao, Yuanyuan Lin, Joel L. Horowitz, Jian Huang

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Nonparametric Inference Under B-Bits Quantization Kexuan Li, Ruiqi Liu, Ganggang Xu, Zuofeng Shang

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Nonparametric Regression for 3D Point Cloud Learning Xinyi Li, Shan Yu, Yueying Wang, Guannan Wang, Li Wang, Ming-Jun Lai

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Nonparametric Regression Using Over-Parameterized Shallow ReLU Neural Networks Yunfei Yang, Ding-Xuan Zhou

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Numerically Stable Sparse Gaussian Processes via Minimum Separation Using Cover Trees Alexander Terenin, David R. Burt, Artem Artemev, Seth Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge

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Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning Ariyan Bighashdel, Daan de Geus, Pavol Jancura, Gijs Dubbelman

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On Causality in Domain Adaptation and Semi-Supervised Learning: An Information-Theoretic Analysis for Parametric Models Xuetong Wu, Mingming Gong, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu

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On Doubly Robust Inference for Double Machine Learning in Semiparametric Regression Oliver Dukes, Stijn Vansteelandt, David Whitney

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On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models Yangjing Zhang, Ying Cui, Bodhisattva Sen, Kim-Chuan Toh

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On Regularized Radon-Nikodym Differentiation Duc Hoan Nguyen, Werner Zellinger, Sergei Pereverzyev

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On Sufficient Graphical Models Bing Li, Kyongwon Kim

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On Tail Decay Rate Estimation of Loss Function Distributions Etrit Haxholli, Marco Lorenzi

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On the Computational and Statistical Complexity of Over-Parameterized Matrix Sensing Jiacheng Zhuo, Jeongyeol Kwon, Nhat Ho, Constantine Caramanis

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On the Computational Complexity of Metropolis-Adjusted Langevin Algorithms for Bayesian Posterior Sampling Rong Tang, Yun Yang

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On the Concentration of the Minimizers of Empirical Risks Paul Escande

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On the Connection Between Lp- and Risk Consistency and Its Implications on Regularized Kernel Methods Hannes Köhler

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On the Convergence of Projected Alternating Maximization for Equitable and Optimal Transport Minhui Huang, Shiqian Ma, Lifeng Lai

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On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks Sebastian Neumayer, Lénaïc Chizat, Michael Unser

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On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains Yicheng Li, Zixiong Yu, Guhan Chen, Qian Lin

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On the Generalization of Stochastic Gradient Descent with Momentum Ali Ramezani-Kebrya, Kimon Antonakopoulos, Volkan Cevher, Ashish Khisti, Ben Liang

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On the Hyperparameters in Stochastic Gradient Descent with Momentum Bin Shi

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On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training Chen Liu, Zhichao Huang, Mathieu Salzmann, Tong Zhang, Sabine Süsstrunk

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On the Intrinsic Structures of Spiking Neural Networks Shao-Qun Zhang, Jia-Yi Chen, Jin-Hui Wu, Gao Zhang, Huan Xiong, Bin Gu, Zhi-Hua Zhou

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On the Learnability of Out-of-Distribution Detection Zhen Fang, Yixuan Li, Feng Liu, Bo Han, Jie Lu

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On the Optimality of Gaussian Kernel Based Nonparametric Tests Against Smooth Alternatives Tong Li, Ming Yuan

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On the Optimality of Misspecified Spectral Algorithms Haobo Zhang, Yicheng Li, Qian Lin

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On the Sample Complexity and Metastability of Heavy-Tailed Policy Search in Continuous Control Amrit Singh Bedi, Anjaly Parayil, Junyu Zhang, Mengdi Wang, Alec Koppel

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On Truthing Issues in Supervised Classification Jonathan K. Su

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Operator Learning Without the Adjoint Nicolas Boullé, Diana Halikias, Samuel E. Otto, Alex Townsend

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Optimal Algorithms for Stochastic Bilevel Optimization Under Relaxed Smoothness Conditions Xuxing Chen, Tesi Xiao, Krishnakumar Balasubramanian

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Optimal Bump Functions for Shallow ReLU Networks: Weight Decay, Depth Separation, Curse of Dimensionality Stephan Wojtowytsch

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Optimal Clustering with Bandit Feedback Junwen Yang, Zixin Zhong, Vincent Y. F. Tan

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Optimal Decision Tree and Adaptive Submodular Ranking with Noisy Outcomes Su Jia, Fatemeh Navidi, Viswanath Nagarajan, R. Ravi

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Optimal First-Order Algorithms as a Function of Inequalities Chanwoo Park, Ernest K. Ryu

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Optimal Learning Policies for Differential Privacy in Multi-Armed Bandits Siwei Wang, Jun Zhu

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Optimal Locally Private Nonparametric Classification with Public Data Yuheng Ma, Hanfang Yang

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Optimal Scaling for the Proximal Langevin Algorithm in High Dimensions Natesh S. Pillai

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Optimal Weighted Random Forests Xinyu Chen, Dalei Yu, Xinyu Zhang

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Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization Sijia Chen, Yu-Jie Zhang, Wei-Wei Tu, Peng Zhao, Lijun Zhang

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Optimistic Search: Change Point Estimation for Large-Scale Data via Adaptive Logarithmic Queries Solt Kovács, Housen Li, Lorenz Haubner, Axel Munk, Peter Bühlmann

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Optimization-Based Causal Estimation from Heterogeneous Environments Mingzhang Yin, Yixin Wang, David M. Blei

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Optimizing Noise for F-Differential Privacy via Anti-Concentration and Stochastic Dominance Jordan Awan, Aishwarya Ramasethu

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Overparametrized Multi-Layer Neural Networks: Uniform Concentration of Neural Tangent Kernel and Convergence of Stochastic Gradient Descent Jiaming Xu, Hanjing Zhu

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PAPAL: A Provable PArticle-Based Primal-Dual ALgorithm for Mixed Nash Equilibrium Shihong Ding, Hanze Dong, Cong Fang, Zhouchen Lin, Tong Zhang

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Parallel-in-Time Probabilistic Numerical ODE Solvers Nathanael Bosch, Adrien Corenflos, Fatemeh Yaghoobi, Filip Tronarp, Philipp Hennig, Simo Särkkä

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Pareto Smoothed Importance Sampling Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry

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Penalized Overdamped and Underdamped Langevin Monte Carlo Algorithms for Constrained Sampling Mert Gurbuzbalaban, Yuanhan Hu, Lingjiong Zhu

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Permuted and Unlinked Monotone Regression in R^d: An Approach Based on Mixture Modeling and Optimal Transport Martin Slawski, Bodhisattva Sen

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Personalized PCA: Decoupling Shared and Unique Features Naichen Shi, Raed Al Kontar

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PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design Alexandre Duval, Victor Schmidt, Santiago Miret, Yoshua Bengio, Alex Hernández-García, David Rolnick

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PirateNets: Physics-Informed Deep Learning with Residual Adaptive Networks Sifan Wang, Bowen Li, Yuhan Chen, Paris Perdikaris

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Policy Gradient Methods in the Presence of Symmetries and State Abstractions Prakash Panangaden, Sahand Rezaei-Shoshtari, Rosie Zhao, David Meger, Doina Precup

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Polygonal Unadjusted Langevin Algorithms: Creating Stable and Efficient Adaptive Algorithms for Neural Networks Dong-Young Lim, Sotirios Sabanis

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Post-Regularization Confidence Bands for Ordinary Differential Equations Xiaowu Dai, Lexin Li

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Power of Knockoff: The Impact of Ranking Algorithm, Augmented Design, and Symmetric Statistic Zheng Tracy Ke, Jun S. Liu, Yucong Ma

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Pre-Trained Gaussian Processes for Bayesian Optimization Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani

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Predictive Inference with Weak Supervision Maxime Cauchois, Suyash Gupta, Alnur Ali, John C. Duchi

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Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization Lorenzo Pacchiardi, Rilwan A. Adewoyin, Peter Dueben, Ritabrata Dutta

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PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell

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Pure Differential Privacy for Functional Summaries with a Laplace-like Process Haotian Lin, Matthew Reimherr

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Pursuit of the Cluster Structure of Network Lasso: Recovery Condition and Non-Convex Extension Shotaro Yagishita, Jun-ya Gotoh

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Random Forest Weighted Local Fréchet Regression with Random Objects Rui Qiu, Zhou Yu, Ruoqing Zhu

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Random Fully Connected Neural Networks as Perturbatively Solvable Hierarchies Boris Hanin

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Random Measure Priors in Bayesian Recovery from Sketches Mario Beraha, Stefano Favaro, Matteo Sesia

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Random Smoothing Regularization in Kernel Gradient Descent Learning Liang Ding, Tianyang Hu, Jiahang Jiang, Donghao Li, Wenjia Wang, Yuan Yao

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Random Subgraph Detection Using Queries Wasim Huleihel, Arya Mazumdar, Soumyabrata Pal

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Rates of Convergence for Density Estimation with Generative Adversarial Networks Nikita Puchkin, Sergey Samsonov, Denis Belomestny, Eric Moulines, Alexey Naumov

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Recursive Estimation of Conditional Kernel Mean Embeddings Ambrus Tamás, Balázs Csanád Csáji

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Regimes of No Gain in Multi-Class Active Learning Gan Yuan, Yunfan Zhao, Samory Kpotufe

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Regret Analysis of Bilateral Trade with a Smoothed Adversary Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Federico Fusco, Stefano Leonardi

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Representation Learning via Manifold Flattening and Reconstruction Michael Psenka, Druv Pai, Vishal Raman, Shankar Sastry, Yi Ma

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Resource-Efficient Neural Networks for Embedded Systems Wolfgang Roth, Günther Schindler, Bernhard Klein, Robert Peharz, Sebastian Tschiatschek, Holger Fröning, Franz Pernkopf, Zoubin Ghahramani

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Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning Sarah Rathnam, Sonali Parbhoo, Siddharth Swaroop, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez

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Revisiting RIP Guarantees for Sketching Operators on Mixture Models Ayoub Belhadji, Rémi Gribonval

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Risk Measures and Upper Probabilities: Coherence and Stratification Christian Fröhlich, Robert C. Williamson

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Robust Black-Box Optimization for Stochastic Search and Episodic Reinforcement Learning Maximilian Hüttenrauch, Gerhard Neumann

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Robust Principal Component Analysis Using Density Power Divergence Subhrajyoty Roy, Ayanendranath Basu, Abhik Ghosh

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Robust Spectral Clustering with Rank Statistics Joshua Cape, Xianshi Yu, Jonquil Z. Liao

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Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds Zhenghao Xu, Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao

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Sample Complexity of Variance-Reduced Distributionally Robust Q-Learning Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou

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Sample-Efficient Adversarial Imitation Learning Dahuin Jung, Hyungyu Lee, Sungroh Yoon

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Scalable High-Dimensional Multivariate Linear Regression for Feature-Distributed Data Shuo-Chieh Huang, Ruey S. Tsay

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Scalable Resampling in Massive Generalized Linear Models via Subsampled Residual Bootstrap Indrila Ganguly, Srijan Sengupta, Sujit Ghosh

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Scaled Conjugate Gradient Method for Nonconvex Optimization in Deep Neural Networks Naoki Sato, Koshiro Izumi, Hideaki Iiduka

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Scaling Instruction-Finetuned Language Models Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, Jason Wei

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Scaling Speech Technology to 1,000+ Languages Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli

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Scaling the Convex Barrier with Sparse Dual Algorithms Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H.S. Torr, M. Pawan Kumar

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Seeded Graph Matching for the Correlated Gaussian Wigner Model via the Projected Power Method Ernesto Araya, Guillaume Braun, Hemant Tyagi

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Semi-Supervised Inference for Block-Wise Missing Data Without Imputation Shanshan Song, Yuanyuan Lin, Yong Zhou

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Sharp Analysis of Power Iteration for Tensor PCA Yuchen Wu, Kangjie Zhou

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Sharpness-Aware Minimization and the Edge of Stability Philip M. Long, Peter L. Bartlett

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Simple Cycle Reservoirs Are Universal Boyu Li, Robert Simon Fong, Peter Tino

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Sparse Graphical Linear Dynamical Systems Emilie Chouzenoux, Victor Elvira

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Sparse NMF with Archetypal Regularization: Computational and Robustness Properties Kayhan Behdin, Rahul Mazumder

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Sparse Recovery with Multiple Data Streams: An Adaptive Sequential Testing Approach Weinan Wang, Bowen Gang, Wenguang Sun

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Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces Rui Wang, Yuesheng Xu, Mingsong Yan

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Spatial Meshing for General Bayesian Multivariate Models Michele Peruzzi, David B. Dunson

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Spectral Analysis of the Neural Tangent Kernel for Deep Residual Networks Yuval Belfer, Amnon Geifman, Meirav Galun, Ronen Basri

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Spectral Learning of Multivariate Extremes Marco Avella Medina, Richard A Davis, Gennady Samorodnitsky

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Spectral Regularized Kernel Goodness-of-Fit Tests Omar Hagrass, Bharath K. Sriperumbudur, Bing Li

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Spherical Rotation Dimension Reduction with Geometric Loss Functions Hengrui Luo, Jeremy E. Purvis, Didong Li

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Split Conformal Prediction and Non-Exchangeable Data Roberto I. Oliveira, Paulo Orenstein, Thiago Ramos, João Vitor Romano

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Stability and L2-Penalty in Model Averaging Hengkun Zhu, Guohua Zou

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Stable and Consistent Density-Based Clustering via Multiparameter Persistence Alexander Rolle, Luis Scoccola

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Stable Implementation of Probabilistic ODE Solvers Nicholas Krämer, Philipp Hennig

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Stage-Aware Learning for Dynamic Treatments Hanwen Ye, Wenzhuo Zhou, Ruoqing Zhu, Annie Qu

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Stationary Kernels and Gaussian Processes on Lie Groups and Their Homogeneous Spaces I: The Compact Case Iskander Azangulov, Andrei Smolensky, Alexander Terenin, Viacheslav Borovitskiy

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Stationary Kernels and Gaussian Processes on Lie Groups and Their Homogeneous Spaces II: Non-Compact Symmetric Spaces Iskander Azangulov, Andrei Smolensky, Alexander Terenin, Viacheslav Borovitskiy

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Statistical Analysis for a Penalized EM Algorithm in High-Dimensional Mixture Linear Regression Model Ning Wang, Xin Zhang, Qing Mai

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Statistical Inference for Fairness Auditing John J. Cherian, Emmanuel J. Candès

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Statistical Optimality of Divide and Conquer Kernel-Based Functional Linear Regression Jiading Liu, Lei Shi

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Stochastic Approximation with Decision-Dependent Distributions: Asymptotic Normality and Optimality Joshua Cutler, Mateo Díaz, Dmitriy Drusvyatskiy

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Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent Benjamin Gess, Sebastian Kassing, Vitalii Konarovskyi

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Stochastic Regularized Majorization-Minimization with Weakly Convex and Multi-Convex Surrogates Hanbaek Lyu

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Stochastic-Constrained Stochastic Optimization with Markovian Data Yeongjong Kim, Dabeen Lee

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Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model Rashmi Ranjan Bhuyan, Adel Javanmard, Sungchul Kim, Gourab Mukherjee, Ryan A. Rossi, Tong Yu, Handong Zhao

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Structured Optimal Variational Inference for Dynamic Latent Space Models Peng Zhao, Anirban Bhattacharya, Debdeep Pati, Bani K. Mallick

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Studying the Interplay Between Information Loss and Operation Loss in Representations for Classification Jorge F. Silva, Felipe Tobar, Mario Vicuña, Felipe Cordova

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Sum-of-Norms Clustering Does Not Separate Nearby Balls Alexander Dunlap, Jean-Christophe Mourrat

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Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee George H. Chen

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Tangential Wasserstein Projections Florian Gunsilius, Meng Hsuan Hsieh, Myung Jin Lee

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Targeted Separation and Convergence with Kernel Discrepancies Alessandro Barp, Carl-Johann Simon-Gabriel, Mark Girolami, Lester Mackey

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Tensor-Train Methods for Sequential State and Parameter Learning in State-Space Models Yiran Zhao, Tiangang Cui

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The Good, the Bad and the Ugly Sides of Data Augmentation: An Implicit Spectral Regularization Perspective Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer, Vidya Muthukumar

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The Loss Landscape of Deep Linear Neural Networks: A Second-Order Analysis El Mehdi Achour, François Malgouyres, Sébastien Gerchinovitz

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The Non-Overlapping Statistical Approximation to Overlapping Group Lasso Mingyu Qi, Tianxi Li

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The Nyström Method for Convex Loss Functions Andrea Della Vecchia, Ernesto De Vito, Jaouad Mourtada, Lorenzo Rosasco

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Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance Lisha Chen, Heshan Fernando, Yiming Ying, Tianyi Chen

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Tight Convergence Rate Bounds for Optimization Under Power Law Spectral Conditions Maksim Velikanov, Dmitry Yarotsky

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Topological Analysis for Detecting Anomalies in Dependent Sequences: Application to Time Series Frédéric Chazal, Clément Levrard, Martin Royer

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Topological Node2vec: Enhanced Graph Embedding via Persistent Homology Yasuaki Hiraoka, Yusuke Imoto, Théo Lacombe, Killian Meehan, Toshiaki Yachimura

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Towards Explainable Evaluation Metrics for Machine Translation Christoph Leiter, Piyawat Lertvittayakumjorn, Marina Fomicheva, Wei Zhao, Yang Gao, Steffen Eger

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Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton

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Towards Unbiased Exploration in Partial Label Learning Zsolt Zombori, Agapi Rissaki, Kristóf Szabó, Wolfgang Gatterbauer, Michael Benedikt

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Trained Transformers Learn Linear Models In-Context Ruiqi Zhang, Spencer Frei, Peter L. Bartlett

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Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width Limit Karl Hajjar, Lénaïc Chizat, Christophe Giraud

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Transfer Learning for Tensor Gaussian Graphical Models Mingyang Ren, Yaoming Zhen, Junhui Wang

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Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST) Jimmy Hickey, Jonathan P. Williams, Emily C. Hector

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Transport-Based Counterfactual Models Lucas De Lara, Alberto González-Sanz, Nicholas Asher, Laurent Risser, Jean-Michel Loubes

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Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components Naichen Shi, Salar Fattahi, Raed Al Kontar

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Two Is Better than One: Regularized Shrinkage of Large Minimum Variance Portfolios Taras Bodnar, Nestor Parolya, Erik Thorsen

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Uncertainty Quantification of MLE for Entity Ranking with Covariates Jianqing Fan, Jikai Hou, Mengxin Yu

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Understanding Entropic Regularization in GANs Daria Reshetova, Yikun Bai, Xiugang Wu, Ayfer Özgür

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Unified Binary and Multiclass Margin-Based Classification Yutong Wang, Clayton Scott

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Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets Benjamin Dupuis, Paul Viallard, George Deligiannidis, Umut Simsekli

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Unlabeled Principal Component Analysis and Matrix Completion Yunzhen Yao, Liangzu Peng, Manolis C. Tsakiris

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Unsupervised Anomaly Detection Algorithms on Real-World Data: How Many Do We Need? Roel Bouman, Zaharah Bukhsh, Tom Heskes

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Unsupervised Tree Boosting for Learning Probability Distributions Naoki Awaya, Li Ma

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Value-Distributional Model-Based Reinforcement Learning Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters

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Variance Estimation in Graphs with the Fused Lasso Oscar Hernan Madrid Padilla

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Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression Joseph Shenouda, Rahul Parhi, Kangwook Lee, Robert D. Nowak

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Variational Estimators of the Degree-Corrected Latent Block Model for Bipartite Networks Yunpeng Zhao, Ning Hao, Ji Zhu

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Virtual-Event-Based Posterior Sampling and Inference for Neyman-Scott Processes Chengkuan Hong, Christian R. Shelton, Jun Zhu

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Volterra Neural Networks (VNNs) Siddharth Roheda, Hamid Krim, Bo Jiang

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Wasserstein Proximal Coordinate Gradient Algorithms Rentian Yao, Xiaohui Chen, Yun Yang

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White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is? Yaodong Yu, Sam Buchanan, Druv Pai, Tianzhe Chu, Ziyang Wu, Shengbang Tong, Hao Bai, Yuexiang Zhai, Benjamin D. Haeffele, Yi Ma

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Win: Weight-Decay-Integrated Nesterov Acceleration for Faster Network Training Pan Zhou, Xingyu Xie, Zhouchen Lin, Kim-Chuan Toh, Shuicheng Yan

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Zeroth-Order Stochastic Approximation Algorithms for DR-Submodular Optimization Yuefang Lian, Xiao Wang, Dachuan Xu, Zhongrui Zhao

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