JMLR 2024

422 papers

Statistical analysis for a penalized EM algorithm in high-dimensional mixture linear regression model

Ning Wang, Xin Zhang, Qing Mai

Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds

Hao Liang, Zhi-Quan Luo

Low-Rank Matrix Estimation in the Presence of Change-Points

Lei Shi, Guanghui Wang, Changliang Zou

A Framework for Improving the Reliability of Black-box Variational Inference

Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins

Understanding Entropic Regularization in GANs

Daria Reshetova, Yikun Bai, Xiugang Wu, Ayfer Ozgur

BenchMARL: Benchmarking Multi-Agent Reinforcement Learning

Matteo Bettini, Amanda Prorok, Vincent Moens

Learning from many trajectories

Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi

Interpretable algorithmic fairness in structured and unstructured data

Hari Bandi, Dimitris Bertsimas, Thodoris Koukouvinos, Sofie Kupiec

FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization

José A. Carrillo, Nicolás García Trillos, Sixu Li, Yuhua Zhu

On the Connection between Lp- and Risk Consistency and its Implications on Regularized Kernel Methods

Hannes Köhler

Pre-trained Gaussian Processes for Bayesian Optimization

Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zachary Nado

Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis

Yuanxing Chen, Qingzhao Zhang, Shuangge Ma, Kuangnan Fang

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

PAMI: An Open-Source Python Library for Pattern Mining

Uday Kiran Rage, Veena Pamalla, Masashi Toyoda, Masaru Kitsuregawa

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

Risk Measures and Upper Probabilities: Coherence and Stratification

Christian Fröhlich, Robert C. Williamson

Parallel-in-Time Probabilistic Numerical ODE Solvers

Nathanael Bosch, Adrien Corenflos, Fatemeh Yaghoobi, Filip Tronarp, Philipp Hennig

Scalable High-Dimensional Multivariate Linear Regression for Feature-Distributed Data

Shuo-Chieh Huang, Ruey S. Tsay

Dropout Regularization Versus l2-Penalization in the Linear Model

Gabriel Clara, Sophie Langer, Johannes Schmidt-Hieber

Efficient Convex Algorithms for Universal Kernel Learning

Aleksandr Talitckii, Brendon Colbert, Matthew M. Peet

Manifold Learning by Mixture Models of VAEs for Inverse Problems

Giovanni S. Alberti, Johannes Hertrich, Matteo Santacesaria, Silvia Sciutto

An Algorithmic Framework for the Optimization of Deep Neural Networks Architectures and Hyperparameters

Julie Keisler, El-Ghazali Talbi, Sandra Claudel, Gilles Cabriel

Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity

Laixi Shi, Yuejie Chi

Grokking phase transitions in learning local rules with gradient descent

Bojan Žunkovič, Enej Ilievski

Unsupervised Tree Boosting for Learning Probability Distributions

Naoki Awaya, Li Ma

Linear Regression With Unmatched Data: A Deconvolution Perspective

Mona Azadkia, Fadoua Balabdaoui

Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width Limit

Karl Hajjar, Lénaïc Chizat, Christophe Giraud

Sharp analysis of power iteration for tensor PCA

Yuchen Wu, Kangjie Zhou

On the Intrinsic Structures of Spiking Neural Networks

Shao-Qun Zhang, Jia-Yi Chen, Jin-Hui Wu, Gao Zhang, Huan Xiong

Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance

Lisha Chen, Heshan Fernando, Yiming Ying, Tianyi Chen

Neural Collapse for Unconstrained Feature Model under Cross-entropy Loss with Imbalanced Data

Wanli Hong, Shuyang Ling

Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables

Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour

Classification of Data Generated by Gaussian Mixture Models Using Deep ReLU Networks

Tian-Yi Zhou, Xiaoming Huo

Differentially Private Topological Data Analysis

Taegyu Kang, Sehwan Kim, Jinwon Sohn, Jordan Awan

On the Optimality of Misspecified Spectral Algorithms

Haobo Zhang, Yicheng Li, Qian Lin

An Entropy-Based Model for Hierarchical Learning

Amir R. Asadi

Optimal Clustering with Bandit Feedback

Junwen Yang, Zixin Zhong, Vincent Y. F. Tan

A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression

Youngseok Kim, Wei Wang, Peter Carbonetto, Matthew Stephens

Spectral Analysis of the Neural Tangent Kernel for Deep Residual Networks

Yuval Belfer, Amnon Geifman, Meirav Galun, Ronen Basri

Permuted and Unlinked Monotone Regression in R^d: an approach based on mixture modeling and optimal transport

Martin Slawski, Bodhisattva Sen

Volterra Neural Networks (VNNs)

Siddharth Roheda, Hamid Krim, Bo Jiang

Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm

Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton

Bayesian Regression Markets

Thomas Falconer, Jalal Kazempour, Pierre Pinson

Sharpness-Aware Minimization and the Edge of Stability

Philip M. Long, Peter L. Bartlett

Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization

Sijia Chen, Yu-Jie Zhang, Wei-Wei Tu, Peng Zhao, Lijun Zhang

Multi-Objective Neural Architecture Search by Learning Search Space Partitions

Yiyang Zhao, Linnan Wang, Tian Guo

Fermat Distances: Metric Approximation, Spectral Convergence, and Clustering Algorithms

Nicolás García Trillos, Anna Little, Daniel Mckenzie, James M. Murphy

Spherical Rotation Dimension Reduction with Geometric Loss Functions

Hengrui Luo, Jeremy E. Purvis, Didong Li

A PDE-based Explanation of Extreme Numerical Sensitivities and Edge of Stability in Training Neural Networks

Yuxin Sun, DONG LAO, Anthony Yezzi, Ganesh Sundaramoorthi

Two is Better Than One: Regularized Shrinkage of Large Minimum Variance Portfolios

Taras Bodnar, Nestor Parolya, Erik Thorsen

Decentralized Natural Policy Gradient with Variance Reduction for Collaborative Multi-Agent Reinforcement Learning

Jinchi Chen, Jie Feng, Weiguo Gao, Ke Wei

Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning

Ilnura Usmanova, Yarden As, Maryam Kamgarpour, Andreas Krause

Cluster-Adaptive Network A/B Testing: From Randomization to Estimation

Yang Liu, Yifan Zhou, Ping Li, Feifang Hu

On the Computational and Statistical Complexity of Over-parameterized Matrix Sensing

JIACHENG ZHUO, Jeongyeol Kwon, nhật Hồ, Constantine Caramanis

Optimization-based Causal Estimation from Heterogeneous Environments

Mingzhang Yin, Yixin Wang, David M. Blei

Optimal Locally Private Nonparametric Classification with Public Data

Yuheng Ma, Hanfang Yang

Learning to Warm-Start Fixed-Point Optimization Algorithms

Rajiv Sambharya, Georgina Hall, Brandon Amos, Bartolomeo Stellato

Nonparametric Regression Using Over-parameterized Shallow ReLU Neural Networks

Yunfei Yang, Ding-Xuan Zhou

Nonparametric Copula Models for Multivariate, Mixed, and Missing Data

Joseph Feldman, Daniel R. Kowal

An Analysis of Quantile Temporal-Difference Learning

Mark Rowland, Rémi Munos, Mohammad Gheshlaghi azar, Yunhao Tang, Georg Ostrovski

Conformal Inference for Online Prediction with Arbitrary Distribution Shifts

Isaac Gibbs, Emmanuel J. Candès

More Efficient Estimation of Multivariate Additive Models Based on Tensor Decomposition and Penalization

Xu Liu, Heng Lian, Jian Huang

A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment

Robert Hu, Dino Sejdinovic, Robin J. Evans

Assessing the Overall and Partial Causal Well-Specification of Nonlinear Additive Noise Models

Christoph Schultheiss, Peter Bühlmann

Simple Cycle Reservoirs are Universal

Boyu Li, Robert Simon Fong, Peter Tino

On the Computational Complexity of Metropolis-Adjusted Langevin Algorithms for Bayesian Posterior Sampling

Rong Tang, Yun Yang

Generalization and Stability of Interpolating Neural Networks with Minimal Width

Hossein Taheri, CHRISTOS THRAMPOULIDIS

Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression

Jiading Liu, Lei Shi

Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations

Yuanyuan Wang, Wei Huang, Mingming Gong, Xi Geng, Tongliang Liu

Robust Black-Box Optimization for Stochastic Search and Episodic Reinforcement Learning

Maximilian Hüttenrauch, Gerhard Neumann

Kernel Thinning

Raaz Dwivedi, Lester Mackey

Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions

Xuxing Chen, Tesi Xiao, Krishnakumar Balasubramanian

Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks

Yunpeng Zhao, Ning Hao, Ji Zhu

Statistical Inference for Fairness Auditing

John J. Cherian, Emmanuel J. Candès

Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning

Yiling Xie, Xiaoming Huo

DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models

Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing

Flexible Bayesian Product Mixture Models for Vector Autoregressions

Suprateek Kundu, Joshua Lukemire

A Variational Approach to Bayesian Phylogenetic Inference

Cheng Zhang, Frederick A. Matsen IV

Fat-Shattering Dimension of k-fold Aggregations

Idan Attias, Aryeh Kontorovich

Unified Binary and Multiclass Margin-Based Classification

Yutong Wang, Clayton Scott

Neural Feature Learning in Function Space

Xiangxiang Xu, Lizhong Zheng

PyGOD: A Python Library for Graph Outlier Detection

Kay Liu, Yingtong Dou, Xueying Ding, Xiyang Hu, Ruitong Zhang

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

Tengyuan Liang

Fixed points of nonnegative neural networks

Tomasz J. Piotrowski, Renato L. G. Cavalcante, Mateusz Gabor

Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks

Fanghui Liu, Leello Dadi, Volkan Cevher

A Survey on Multi-player Bandits

Etienne Boursier, Vianney Perchet

Transport-based Counterfactual Models

Lucas De Lara, Alberto Gonzalez-Sanz, Nicholas Asher, Laurent Risser, Jean-Michel Loubes

Adaptive Latent Feature Sharing for Piecewise Linear Dimensionality Reduction

Adam Farooq, Yordan P. Raykov, Petar Raykov, Max A. Little

Topological Node2vec: Enhanced Graph Embedding via Persistent Homology

Yasuaki Hiraoka, Yusuke Imoto, Theo Lacombe, Killian Meehan, Toshiaki Yachimura

Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length

Kateřina Hlaváčková-Schindler, Anna Melnykova, Irene Tubikanec

Representation Learning via Manifold Flattening and Reconstruction

Michael Psenka, Druv Pai, Vishal Raman, Shankar Sastry, Yi Ma

Bagging Provides Assumption-free Stability

Jake A. Soloff, Rina Foygel Barber, Rebecca Willett

Fairness guarantees in multi-class classification with demographic parity

Christophe Denis, Romuald Elie, Mohamed Hebiri, Francois Hu

Regimes of No Gain in Multi-class Active Learning

Gan Yuan, Yunfan Zhao, Samory Kpotufe

Learning Optimal Dynamic Treatment Regimens Subject to Stagewise Risk Controls

Mochuan Liu, Yuanjia Wang, Haoda Fu, Donglin Zeng

Margin-Based Active Learning of Classifiers

Marco Bressan, Nicolò Cesa-bianchi, Silvio Lattanzi, Andrea Paudice

Random Subgraph Detection Using Queries

Wasim Huleihel, Arya Mazumdar, Soumyabrata Pal

Classification with Deep Neural Networks and Logistic Loss

Zihan Zhang, Lei Shi, Ding-Xuan Zhou

Spectral learning of multivariate extremes

Marco Avella Medina, Richard A Davis, Gennady Samorodnitsky

Sum-of-norms clustering does not separate nearby balls

Alexander Dunlap, Jean-Christophe Mourrat