JMLR 2023

401 papers

Variational Inverting Network for Statistical Inverse Problems of Partial Differential Equations

Junxiong Jia, Yanni Wu, Peijun Li, Deyu Meng

Model-based Causal Discovery for Zero-Inflated Count Data

Junsouk Choi, Yang Ni

Q-Learning for MDPs with General Spaces: Convergence and Near Optimality via Quantization under Weak Continuity

Ali Kara, Naci Saldi, Serdar Yüksel

CodaLab Competitions: An Open Source Platform to Organize Scientific Challenges

Adrien Pavao, Isabelle Guyon, Anne-Catherine Letournel, Dinh-Tuan Tran, Xavier Baro

Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency

Tetiana Gorbach, Xavier de Luna, Juha Karvanen, Ingeborg Waernbaum

Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data

Vaidotas Simkus, Benjamin Rhodes, Michael U. Gutmann

Clustering and Structural Robustness in Causal Diagrams

Santtu Tikka, Jouni Helske, Juha Karvanen

MMD Aggregated Two-Sample Test

Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj

Divide-and-Conquer Fusion

Ryan S.Y. Chan, Murray Pollock, Adam M. Johansen, Gareth O. Roberts

PAC-learning for Strategic Classification

Ravi Sundaram, Anil Vullikanti, Haifeng Xu, Fan Yao

Insights into Ordinal Embedding Algorithms: A Systematic Evaluation

Leena Chennuru Vankadara, Michael Lohaus, Siavash Haghiri, Faiz Ul Wahab, Ulrike Von Luxburg

Clustering with Tangles: Algorithmic Framework and Theoretical Guarantees

Solveig Klepper, Christian Elbracht, Diego Fioravanti, Jakob Kneip, Luca Rendsburg

Random Feature Neural Networks Learn Black-Scholes Type PDEs Without Curse of Dimensionality

Lukas Gonon

The Proximal ID Algorithm

Ilya Shpitser, Zach Wood-Doughty, Eric J. Tchetgen Tchetgen

Quantifying Network Similarity using Graph Cumulants

Gecia Bravo-Hermsdorff, Lee M. Gunderson, Pierre-André Maugis, Carey E. Priebe

Learning an Explicit Hyper-parameter Prediction Function Conditioned on Tasks

Jun Shu, Deyu Meng, Zongben Xu

On the Theoretical Equivalence of Several Trade-Off Curves Assessing Statistical Proximity

Rodrigue Siry, Ryan Webster, Loic Simon, Julien Rabin

Metrizing Weak Convergence with Maximum Mean Discrepancies

Carl-Johann Simon-Gabriel, Alessandro Barp, Bernhard Schölkopf, Lester Mackey

Quasi-Equivalence between Width and Depth of Neural Networks

Fenglei Fan, Rongjie Lai, Ge Wang

Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding

Justin Grimmer, Dean Knox, Brandon Stewart

Factor Graph Neural Networks

Zhen Zhang, Mohammed Haroon Dupty, Fan Wu, Javen Qinfeng Shi, Wee Sun Lee

Dropout Training is Distributionally Robust Optimal

Jose Blanchet, Yang Kang, José Luis Montiel Olea, Viet Anh Nguyen, Xuhui Zhang

Variational Inference for Deblending Crowded Starfields

Runjing Liu, Jon D. Mcauliffe, Jeffrey Regier, The LSST Dark Energy Science Collaboration

F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning

Wenhao Li, Bo Jin, Xiangfeng Wang, Junchi Yan, Hongyuan Zha

Comprehensive Algorithm Portfolio Evaluation using Item Response Theory

Sevvandi Kandanaarachchi, Kate Smith-Miles

Evaluating Instrument Validity using the Principle of Independent Mechanisms

Patrick F. Burauel

Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity

Kaiqing Zhang, Sham M. Kakade, Tamer Basar, Lin F. Yang

Posterior Consistency for Bayesian Relevance Vector Machines

Xiao Fang, Malay Ghosh

From Classification Accuracy to Proper Scoring Rules: Elicitability of Probabilistic Top List Predictions

Johannes Resin

Beyond the Golden Ratio for Variational Inequality Algorithms

Ahmet Alacaoglu, Axel Böhm, Yura Malitsky

Incremental Learning in Diagonal Linear Networks

Raphaël Berthier

Small Transformers Compute Universal Metric Embeddings

Anastasis Kratsios, Valentin Debarnot, Ivan Dokmanić

DART: Distance Assisted Recursive Testing

Xuechan Li, Anthony D. Sung, Jichun Xie

Inference on the Change Point under a High Dimensional Covariance Shift

Abhishek Kaul, Hongjin Zhang, Konstantinos Tsampourakis, George Michailidis

Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start

Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo

A Parameter-Free Conditional Gradient Method for Composite Minimization under Hölder Condition

Masaru Ito, Zhaosong Lu, Chuan He

Robust Methods for High-Dimensional Linear Learning

Ibrahim Merad, Stephane Gaiffas

A Framework and Benchmark for Deep Batch Active Learning for Regression

David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart

Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification

Gavin Zhang, Salar Fattahi, Richard Y. Zhang

Flexible Model Aggregation for Quantile Regression

Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani

q-Learning in Continuous Time

Yanwei Jia, Xun Yu Zhou

Multivariate Soft Rank via Entropy-Regularized Optimal Transport: Sample Efficiency and Generative Modeling

Shoaib Bin Masud, Matthew Werenski, James M. Murphy, Shuchin Aeron

Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations

Arnab Ganguly, Riten Mitra, Jinpu Zhou

Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees

Hamid Reza Feyzmahdavian, Mikael Johansson

Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the in the O(epsilon^(-7/4)) Complexity

Huan Li, Zhouchen Lin

Integrating Random Effects in Deep Neural Networks

Giora Simchoni, Saharon Rosset

Adaptive Data Depth via Multi-Armed Bandits

Tavor Baharav, Tze Leung Lai

Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees

Jonathan Brophy, Zayd Hammoudeh, Daniel Lowd

Consistent Model-based Clustering using the Quasi-Bernoulli Stick-breaking Process

Cheng Zeng, Jeffrey W Miller, Leo L Duan

Selective inference for k-means clustering

Yiqun T. Chen, Daniela M. Witten

Generalization error bounds for multiclass sparse linear classifiers

Tomer Levy, Felix Abramovich

MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning

Ming Zhou, Ziyu Wan, Hanjing Wang, Muning Wen, Runzhe Wu

Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning

Titouan Vayer, Remi Gribonval

Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition

Zhishuai Guo, Yan Yan, Zhuoning Yuan, Tianbao Yang

Stochastic Optimization under Distributional Drift

Joshua Cutler, Dmitriy Drusvyatskiy, Zaïd Harchaoui

Off-Policy Actor-Critic with Emphatic Weightings

Eric Graves, Ehsan Imani, Raksha Kumaraswamy, Martha White

Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning

Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang

Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering

Noirrit Kiran Chandra, Antonio Canale, David B. Dunson

Large sample spectral analysis of graph-based multi-manifold clustering

Nicolás García Trillos, Pengfei He, Chenghui Li

On Tilted Losses in Machine Learning: Theory and Applications

Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith

Optimal Convergence Rates for Distributed Nystroem Approximation

Jian Li, Yong Liu, Weiping Wang

Jump Interval-Learning for Individualized Decision Making with Continuous Treatments

Hengrui Cai, Chengchun Shi, Rui Song, Wenbin Lu

Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games

Ben Hambly, Renyuan Xu, Huining Yang

Asymptotics of Network Embeddings Learned via Subsampling

Andrew Davison, Morgane Austern

Implicit Bias of Gradient Descent for Mean Squared Error Regression with Two-Layer Wide Neural Networks

Hui Jin, Guido Montúfar

Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection

Wenhao Li, Ningyuan Chen, L. Jeff Hong

Sparse GCA and Thresholded Gradient Descent

Sheng Gao, Zongming Ma

MARS: A Second-Order Reduction Algorithm for High-Dimensional Sparse Precision Matrices Estimation

Qian Li, Binyan Jiang, Defeng Sun

Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators

Benjamin Jakubowski, Sriram Somanchi, Edward McFowland III, Daniel B. Neill

Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data

Di Wang, Lijie Hu, Huanyu Zhang, Marco Gaboardi, Jinhui Xu

A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition

Patricia Wollstadt, Sebastian Schmitt, Michael Wibral

Combinatorial Optimization and Reasoning with Graph Neural Networks

Quentin Cappart, Didier Chetelat, Elias B. Khalil, Andrea Lodi, Christopher Morris

A First Look into the Carbon Footprint of Federated Learning

Xinchi Qiu, Titouan Parcollet, Javier Fernández-Marqués, Pedro P. B. Gusmao, Yan Gao

An Eigenmodel for Dynamic Multilayer Networks

Joshua Daniel Loyal, Yuguo Chen

Graph Clustering with Graph Neural Networks

Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller

Euler-Lagrange Analysis of Generative Adversarial Networks

Siddarth Asokan, Chandra Sekhar Seelamantula

Statistical Robustness of Empirical Risks in Machine Learning

Shaoyan Guo, Huifu Xu, Liwei Zhang

HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation

Weijie J. Su, Yuancheng Zhu

Benign overfitting in ridge regression

Alexander Tsigler, Peter L. Bartlett

Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities

Brian R. Bartoldson, Bhavya Kailkhura, Davis Blalock

Minimal Width for Universal Property of Deep RNN

Chang hoon Song, Geonho Hwang, Jun ho Lee, Myungjoo Kang

Maximum likelihood estimation in Gaussian process regression is ill-posed

Toni Karvonen, Chris J. Oates

An Annotated Graph Model with Differential Degree Heterogeneity for Directed Networks

Stefan Stein, Chenlei Leng

A Unified Framework for Optimization-Based Graph Coarsening

Manoj Kumar, Anurag Sharma, Sandeep Kumar

Deep linear networks can benignly overfit when shallow ones do

Niladri S. Chatterji, Philip M. Long

SQLFlow: An Extensible Toolkit Integrating DB and AI

Jun Zhou, Ke ZHANG, Lin Wang, Hua Wu, Yi Wang

Learning Good State and Action Representations for Markov Decision Process via Tensor Decomposition

Chengzhuo Ni, Yaqi Duan, Munther Dahleh, Mengdi Wang, Anru R. Zhang

Generalization Bounds for Adversarial Contrastive Learning

Xin Zou, Weiwei Liu

The Implicit Bias of Benign Overfitting

Ohad Shamir

The Hyperspherical Geometry of Community Detection: Modularity as a Distance

Martijn Gösgens, Remco van der Hofstad, Nelly Litvak

FLIP: A Utility Preserving Privacy Mechanism for Time Series

Tucker McElroy, Anindya Roy, Gaurab Hore

A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates

Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi

Dimensionless machine learning: Imposing exact units equivariance

Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu

Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priors

Michail Spitieris, Ingelin Steinsland

Risk Bounds for Positive-Unlabeled Learning Under the Selected At Random Assumption

Olivier Coudray, Christine Keribin, Pascal Massart, Patrick Pamphile

Concentration analysis of multivariate elliptic diffusions

Lukas Trottner, Cathrine Aeckerle-Willems, Claudia Strauch

Knowledge Hypergraph Embedding Meets Relational Algebra

Bahare Fatemi, Perouz Taslakian, David Vázquez, David Poole

Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics

Mu Niu, Zhenwen Dai, Pokman Cheung, Yizhu Wang

Sparse Training with Lipschitz Continuous Loss Functions and a Weighted Group L0-norm Constraint

Michael R. Metel

Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments

Haixu Ma, Donglin Zeng, Yufeng Liu