JMLR 2024

422 papers

Stability and L2-penalty in Model Averaging

Hengkun Zhu, Guohua Zou

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

Optimal Weighted Random Forests

Xinyu Chen, Dalei Yu, Xinyu Zhang

Efficient Active Manifold Identification via Accelerated Iteratively Reweighted Nuclear Norm Minimization

Hao Wang, Ye Wang, Xiangyu Yang

Empirical Design in Reinforcement Learning

Andrew Patterson, Samuel Neumann, Martha White, Adam White

A Data-Adaptive RKHS Prior for Bayesian Learning of Kernels in Operators

Neil K. Chada, Quanjun Lang, Fei Lu, Xiong Wang

GGD: Grafting Gradient Descent

Yanjing Feng, Yongdao Zhou

Debiasing Evaluations That Are Biased by Evaluations

Jingyan Wang, Ivan Stelmakh, Yuting Wei, Nihar Shah

Optimal Learning Policies for Differential Privacy in Multi-armed Bandits

Siwei Wang, Jun Zhu

Data-Efficient Policy Evaluation Through Behavior Policy Search

Josiah P. Hanna, Yash Chandak, Philip S. Thomas, Martha White, Peter Stone

Just Wing It: Near-Optimal Estimation of Missing Mass in a Markovian Sequence

Ashwin Pananjady, Vidya Muthukumar, Andrew Thangaraj

Estimating the Replication Probability of Significant Classification Benchmark Experiments

Daniel Berrar

Causal Discovery with Generalized Linear Models through Peeling Algorithms

Minjie Wang, Xiaotong Shen, Wei Pan

Spectral Regularized Kernel Goodness-of-Fit Tests

Omar Hagrass, Bharath K. Sriperumbudur, Bing Li

Matryoshka Policy Gradient for Entropy-Regularized RL: Convergence and Global Optimality

François G. Ged, Maria Han Veiga

Non-Euclidean Monotone Operator Theory and Applications

Alexander Davydov, Saber Jafarpour, Anton V. Proskurnikov, Francesco Bullo

Stochastic Regularized Majorization-Minimization with weakly convex and multi-convex surrogates

Hanbaek Lyu

Pure Differential Privacy for Functional Summaries with a Laplace-like Process

Haotian Lin, Matthew Reimherr

Sparse Recovery With Multiple Data Streams: An Adaptive Sequential Testing Approach

Weinan Wang, Bowen Gang, Wenguang Sun

Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning

Luofeng Liao, Zuyue Fu, Zhuoran Yang, Yixin Wang, Dingli Ma

Identifying Causal Eﬀects using Instrumental Time Series: Nuisance IV and Correcting for the Past

Nikolaj Thams, Rikke Søndergaard, Sebastian Weichwald, Jonas Peters

RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control

Jonas Eschmann, Dario Albani, Giuseppe Loianno

White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is?

Yaodong Yu, Sam Buchanan, Druv Pai, Tianzhe Chu, Ziyang Wu

Commutative Scaling of Width and Depth in Deep Neural Networks

Soufiane Hayou

Value-Distributional Model-Based Reinforcement Learning

Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters

Optimistic Search: Change Point Estimation for Large-scale Data via Adaptive Logarithmic Queries

Solt Kovács, Housen Li, Lorenz Haubner, Axel Munk, Peter Bühlmann

PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization

Qiqi Duan, Guochen Zhou, Chang Shao, Zhuowei Wang, Mingyang Feng

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

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

Learning and scoring Gaussian latent variable causal models with unknown additive interventions

Armeen Taeb, Juan L. Gamella, Christina Heinze-Deml, Peter Bühlmann

Non-splitting Neyman-Pearson Classifiers

Jingming Wang, Lucy Xia, Zhigang Bao, Xin Tong

Studying the Interplay between Information Loss and Operation Loss in Representations for Classification

Jorge F. Silva, Felipe Tobar, Mario Vicuña, Felipe Cordova

skscope: Fast Sparsity-Constrained Optimization in Python

Zezhi Wang, Junxian Zhu, Xueqin Wang, Jin Zhu, Huiyang Pen

aeon: a Python Toolkit for Learning from Time Series

Matthew Middlehurst, Ali Ismail-Fawaz, Antoine Guillaume, Christopher Holder, David Guijo-Rubio

Compressed and distributed least-squares regression: convergence rates with applications to federated learning

Constantin Philippenko, Aymeric Dieuleveut

Contamination-source based K-sample clustering

Xavier Milhaud, Denys Pommeret, Yahia Salhi, Pierre Vandekerkhove

Measuring Sample Quality in Algorithms for Intractable Normalizing Function Problems

Bokgyeong Kang, John Hughes, Murali Haran

OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research

Jiaming Ji, Jiayi Zhou, Borong Zhang, Juntao Dai, Xuehai Pan

Random Smoothing Regularization in Kernel Gradient Descent Learning

Liang Ding, Tianyang Hu, Jiahang Jiang, Donghao Li, Wenjia Wang

MLRegTest: A Benchmark for the Machine Learning of Regular Languages

Sam van der Poel, Dakotah Lambert, Kalina Kostyszyn, Tiantian Gao, Rahul Verma

A tensor factorization model of multilayer network interdependence

Izabel Aguiar, Dane Taylor, Johan Ugander

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

Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case

Iskander Azangulov, Andrei Smolensky, Alexander Terenin, Viacheslav Borovitskiy

On Doubly Robust Inference for Double Machine Learning in Semiparametric Regression

Oliver Dukes, Stijn Vansteelandt, David Whitney

Deep Neural Network Approximation of Invariant Functions through Dynamical Systems

Qianxiao Li, Ting Lin, Zuowei Shen

A Statistical Experimental Design Method for Constructing Deterministic Sensing Matrices for Compressed Sensing

Youran Qi, Xu He, Tzu-Hsiang Hung, Peter Chien

Functional optimal transport: regularized map estimation and domain adaptation for functional data

Jiacheng Zhu, Aritra Guha, Dat Do, Mengdi Xu, Xuanlong Nguyen

Desiderata for Representation Learning: A Causal Perspective

Yixin Wang, Michael I. Jordan

Accelerated Gradient Tracking over Time-varying Graphs for Decentralized Optimization

Huan Li, Zhouchen Lin

Pearl: A Production-Ready Reinforcement Learning Agent

Zheqing Zhu, Rodrigo de Salvo Braz, Jalaj Bhandari, Daniel Jiang, Yi Wan

Boundary constrained Gaussian processes for robust physics-informed machine learning of linear partial differential equations

David Dalton, Alan Lazarus, Hao Gao, Dirk Husmeier

Almost Sure Convergence Rates Analysis and Saddle Avoidance of Stochastic Gradient Methods

Jun Liu, Ye Yuan

False discovery proportion envelopes with m-consistency

Meah Iqraa, Blanchard Gilles, Roquain Etienne

Wasserstein Proximal Coordinate Gradient Algorithms

Rentian Yao, Xiaohui Chen, Yun Yang

Concentration and Moment Inequalities for General Functions of Independent Random Variables with Heavy Tails

Shaojie Li, Yong Liu

Random Fully Connected Neural Networks as Perturbatively Solvable Hierarchies

Boris Hanin

On Regularized Radon-Nikodym Differentiation

Duc Hoan Nguyen, Werner Zellinger, Sergei Pereverzyev

pgmpy: A Python Toolkit for Bayesian Networks

Ankur Ankan, Johannes Textor

Recursive Estimation of Conditional Kernel Mean Embeddings

Ambrus Tamás, Balázs Csanád Csáji

Penalized Overdamped and Underdamped Langevin Monte Carlo Algorithms for Constrained Sampling

Mert Gurbuzbalaban, Yuanhan Hu, Lingjiong Zhu

Fast Rates in Pool-Based Batch Active Learning

Claudio Gentile, Zhilei Wang, Tong Zhang

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

Mean-Field Approximation of Cooperative Constrained Multi-Agent Reinforcement Learning (CMARL)

Washim Uddin Mondal, Vaneet Aggarwal, Satish V. Ukkusuri

Structured Optimal Variational Inference for Dynamic Latent Space Models

Peng Zhao, Anirban Bhattacharya, Debdeep Pati, Bani K. Mallick

Stable and Consistent Density-Based Clustering via Multiparameter Persistence

Alexander Rolle, Luis Scoccola

Faster Randomized Methods for Orthogonality Constrained Problems

Boris Shustin, Haim Avron

Estimation of Sparse Gaussian Graphical Models with Hidden Clustering Structure

Meixia LIN, Defeng Sun, Kim-Chuan Toh, Chengjing Wang

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

PromptBench: A Unified Library for Evaluation of Large Language Models

Kaijie Zhu, Qinlin Zhao, Hao Chen, Jindong Wang, Xing Xie

Gaussian Interpolation Flows

Yuan Gao, Jian Huang, and Yuling Jiao

Gaussian Mixture Models with Rare Events

Xuetong Li, Jing Zhou, Hansheng Wang

On the Concentration of the Minimizers of Empirical Risks

Paul Escande

Variance estimation in graphs with the fused lasso

OSCAR HERNAN MADRID PADILLA

Random measure priors in Bayesian recovery from sketches

Mario Beraha, Stefano Favaro, Matteo Sesia

From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs

Lorenz Richter, Leon Sallandt, Nikolas Nüsken

Label Alignment Regularization for Distribution Shift

Ehsan Imani, Guojun Zhang, Runjia Li, Jun Luo, Pascal Poupart

Fairness in Survival Analysis with Distributionally Robust Optimization

Shu Hu, George H. Chen

FineMorphs: Affine-Diffeomorphic Sequences for Regression

Michele Lohr, Laurent Younes

Tensor-train methods for sequential state and parameter learning in state-space models

Yiran Zhao, Tiangang Cui

Memory of recurrent networks: Do we compute it right?

Giovanni Ballarin, Lyudmila Grigoryeva, Juan-Pablo Ortega

The Loss Landscape of Deep Linear Neural Networks: a Second-order Analysis

El Mehdi Achour, François Malgouyres, Sébastien Gerchinovitz

High Probability Convergence Bounds for Non-convex Stochastic Gradient Descent with Sub-Weibull Noise

Liam Madden, Emiliano Dall'Anese, Stephen Becker

Euler Characteristic Tools for Topological Data Analysis

Olympio Hacquard, Vadim Lebovici

Depth Degeneracy in Neural Networks: Vanishing Angles in Fully Connected ReLU Networks on Initialization

Cameron Jakub, Mihai Nica

Fortuna: A Library for Uncertainty Quantification in Deep Learning

Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias Seeger, Andrew Gordon Wilson

Characterization of translation invariant MMD on Rd and connections with Wasserstein distances

Thibault Modeste, Clément Dombry

On the Hyperparameters in Stochastic Gradient Descent with Momentum

Bin Shi

Improved Random Features for Dot Product Kernels

Jonas Wacker, Motonobu Kanagawa, Maurizio Filippone

Regret Analysis of Bilateral Trade with a Smoothed Adversary

Nicolò Cesa-bianchi, Tommaso Cesari, Roberto Colomboni, Federico Fusco, Stefano Leonardi

Invariant Physics-Informed Neural Networks for Ordinary Differential Equations

Shivam Arora, Alex Bihlo, Francis Valiquette

Distribution Learning via Neural Differential Equations: A Nonparametric Statistical Perspective

Youssef Marzouk, Zhi (Robert) Ren, Sven Wang, Jakob Zech

Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression

Joseph Shenouda, Rahul Parhi, Kangwook Lee, Robert D. Nowak

Individual-centered Partial Information in Social Networks

Xiao Han, Y. X. Rachel Wang, Qing Yang, Xin Tong

Data-driven Automated Negative Control Estimation (DANCE): Search for, Validation of, and Causal Inference with Negative Controls

Erich Kummerfeld, Jaewon Lim, Xu Shi

Continuous Prediction with Experts' Advice

Nicholas J. A. Harvey, Christopher Liaw, Victor S. Portella

Memory-Efficient Sequential Pattern Mining with Hybrid Tries

Amin Hosseininasab, Willem-Jan van Hoeve, Andre A. Cire

Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds

Zhenghao Xu, Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao

Split Conformal Prediction and Non-Exchangeable Data

Roberto I. Oliveira, Paulo Orenstein, Thiago Ramos, João Vitor Romano

Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model

Rashmi Ranjan Bhuyan, Adel Javanmard, Sungchul Kim, Gourab Mukherjee, Ryan A. Rossi

Sparse Graphical Linear Dynamical Systems

Emilie Chouzenoux, Victor Elvira