JMLR 2023

401 papers

Distributed Community Detection in Large Networks

Sheng Zhang, Rui Song, Wenbin Lu, Ji Zhu

Foundation Models and Fair Use

Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto, Mark A. Lemley

Boosting Multi-agent Reinforcement Learning via Contextual Prompting

Yue Deng, Zirui Wang, Xi Chen, Yin Zhang

Finding Groups of Cross-Correlated Features in Bi-View Data

Miheer Dewaskar, John Palowitch, Mark He, Michael I. Love, Andrew B. Nobel

Bayesian Spanning Tree: Estimating the Backbone of the Dependence Graph

Leo L. Duan, David B. Dunson

RVCL: Evaluating the Robustness of Contrastive Learning via Verification

Zekai Wang, Weiwei Liu

Adaptive Learning of Density Ratios in RKHS

Werner Zellinger, Stefan Kindermann, Sergei V. Pereverzyev

Revisiting inference after prediction

Keshav Motwani, Daniela Witten

A Unified Approach to Controlling Implicit Regularization via Mirror Descent

Haoyuan Sun, Khashayar Gatmiry, Kwangjun Ahn, Navid Azizan

Instance-Dependent Confidence and Early Stopping for Reinforcement Learning

Eric Xia, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan

Hierarchical Kernels in Deep Kernel Learning

Wentao Huang, Houbao Lu, Haizhang Zhang

A Scalable and Efficient Iterative Method for Copying Machine Learning Classifiers

Nahuel Statuto, Irene Unceta, Jordi Nin, Oriol Pujol

Semiparametric Inference Using Fractional Posteriors

Alice L'Huillier, Luke Travis, Ismaël Castillo, Kolyan Ray

Fourier Neural Operator with Learned Deformations for PDEs on General Geometries

Zongyi Li, Daniel Zhengyu Huang, Burigede Liu, Anima Anandkumar

Distributed Statistical Inference under Heterogeneity

Jia Gu, Song Xi Chen

Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice

Jonas Rothfuss, Martin Josifoski, Vincent Fortuin, Andreas Krause

Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning

Zihao Li, Boyi Liu, Zhuoran Yang, Zhaoran Wang, Mengdi Wang

On Unbalanced Optimal Transport: Gradient Methods, Sparsity and Approximation Error

Quang Minh Nguyen, Hoang H. Nguyen, Yi Zhou, Lam M. Nguyen

Over-parameterized Deep Nonparametric Regression for Dependent Data with Its Applications to Reinforcement Learning

Xingdong Feng, Yuling Jiao, Lican Kang, Baqun Zhang, Fan Zhou

A Novel Integer Linear Programming Approach for Global L0 Minimization

Diego Delle Donne, Matthieu Kowalski, Leo Liberti

Low-rank Tensor Estimation via Riemannian Gauss-Newton: Statistical Optimality and Second-Order Convergence

Yuetian Luo, Anru R. Zhang

Randomized Spectral Co-Clustering for Large-Scale Directed Networks

Xiao Guo, Yixuan Qiu, Hai Zhang, Xiangyu Chang

On Learning Rates and Schrödinger Operators

Bin Shi, Weijie Su, Michael I. Jordan

Principled Out-of-Distribution Detection via Multiple Testing

Akshayaa Magesh, Venugopal V. Veeravalli, Anirban Roy, Susmit Jha

Scaling Up Models and Data with t5x and seqio

Adam Roberts, Hyung Won Chung, Gaurav Mishra, Anselm Levskaya, James Bradbury

On the Dynamics Under the Unhinged Loss and Beyond

Xiong Zhou, Xianming Liu, Hanzhang Wang, Deming Zhai, Junjun Jiang

Set-valued Classification with Out-of-distribution Detection for Many Classes

Zhou Wang, Xingye Qiao

Diffusion Bridge Mixture Transports, Schrödinger Bridge Problems and Generative Modeling

Stefano Peluchetti

Multilevel CNNs for Parametric PDEs

Cosmas Heiß, Ingo Gühring, Martin Eigel

A Unified Recipe for Deriving (Time-Uniform) PAC-Bayes Bounds

Ben Chugg, Hongjian Wang, Aaditya Ramdas

Decentralized Robust V-learning for Solving Markov Games with Model Uncertainty

Shaocong Ma, Ziyi Chen, Shaofeng Zou, Yi Zhou

Densely Connected G-invariant Deep Neural Networks with Signed Permutation Representations

Devanshu Agrawal, James Ostrowski

A Permutation-Free Kernel Independence Test

Shubhanshu Shekhar, Ilmun Kim, Aaditya Ramdas

LapGym - An Open Source Framework for Reinforcement Learning in Robot-Assisted Laparoscopic Surgery

Paul Maria Scheikl, Balazs Gyenes, Rayan Younis, Christoph Haas, Gerhard Neumann

TorchOpt: An Efficient Library for Differentiable Optimization

Jie Ren*, Xidong Feng*, Bo Liu*, Xuehai Pan*, Yao Fu

Confidence and Uncertainty Assessment for Distributional Random Forests

Jeffrey Näf, Corinne Emmenegger, Peter Bühlmann, Nicolai Meinshausen

Hard-Constrained Deep Learning for Climate Downscaling

Paula Harder, Alex Hernández-García, Venkatesh Ramesh, Qidong Yang, Prasanna Sattegeri

Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set

Gabriel Laberge, Yann Pequignot, Alexandre Mathieu, Foutse Khomh, Mario Marchand

Avalanche: A PyTorch Library for Deep Continual Learning

Antonio Carta, Lorenzo Pellegrini, Andrea Cossu, Hamed Hemati, Vincenzo Lomonaco

Discovering Salient Neurons in deep NLP models

Nadir Durrani, Fahim Dalvi, Hassan Sajjad

Differentially Private Hypothesis Testing for Linear Regression

Daniel G. Alabi, Salil P. Vadhan

Attribution-based Explanations that Provide Recourse Cannot be Robust

Hidde Fokkema, Rianne de Heide, Tim van Erven

A Unified Theory of Diversity in Ensemble Learning

Danny Wood, Tingting Mu, Andrew M. Webb, Henry W. J. Reeve, Mikel Lujan

Optimal Parameter-Transfer Learning by Semiparametric Model Averaging

Xiaonan Hu, Xinyu Zhang

Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces

Jonathan W. Siegel

MAUVE Scores for Generative Models: Theory and Practice

Krishna Pillutla, Lang Liu, John Thickstun, Sean Welleck, Swabha Swayamdipta

Beyond Spectral Gap: The Role of the Topology in Decentralized Learning

Thijs Vogels, Hadrien Hendrikx, Martin Jaggi

A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models

Ehsan Mokhtarian, Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash

Limitations on approximation by deep and shallow neural networks

Guergana Petrova, Przemyslaw Wojtaszczyk

Group SLOPE Penalized Low-Rank Tensor Regression

Yang Chen, Ziyan Luo

Modular Regression: Improving Linear Models by Incorporating Auxiliary Data

Ying Jin, Dominik Rothenhäusler

Robust High-Dimensional Low-Rank Matrix Estimation: Optimal Rate and Data-Adaptive Tuning

Xiaolong Cui, Lei Shi, Wei Zhong, Changliang Zou

Instance-Dependent Generalization Bounds via Optimal Transport

Songyan Hou, Parnian Kassraie, Anastasis Kratsios, Andreas Krause, Jonas Rothfuss

Conformal Frequency Estimation using Discrete Sketched Data with Coverage for Distinct Queries

Matteo Sesia, Stefano Favaro, Edgar Dobriban

Implicit Regularization and Entrywise Convergence of Riemannian Optimization for Low Tucker-Rank Tensor Completion

Haifeng Wang, Jinchi Chen, Ke Wei

Linear Partial Monitoring for Sequential Decision Making: Algorithms, Regret Bounds and Applications

Johannes Kirschner, Tor Lattimore, Andreas Krause

Radial Basis Approximation of Tensor Fields on Manifolds: From Operator Estimation to Manifold Learning

John Harlim, Shixiao Willing Jiang, John Wilson Peoples

Large data limit of the MBO scheme for data clustering: convergence of the dynamics

Tim Laux, Jona Lelmi

Accelerated Primal-Dual Mirror Dynamics for Centralized and Distributed Constrained Convex Optimization Problems

You Zhao, Xiaofeng Liao, Xing He, Mingliang Zhou, Chaojie Li

The Geometry and Calculus of Losses

Robert C. Williamson, Zac Cranko

A Bayesian Bradley-Terry model to compare multiple ML algorithms on multiple data sets

Jacques Wainer

Topological Hidden Markov Models

Adam B Kashlak, Prachi Loliencar, Giseon Heo

Compression, Generalization and Learning

Marco C. Campi, Simone Garatti

Community Recovery in the Geometric Block Model

Sainyam Galhotra, Arya Mazumdar, Soumyabrata Pal, Barna Saha

The Art of BART: Minimax Optimality over Nonhomogeneous Smoothness in High Dimension

Seonghyun Jeong, Veronika Ročková

Finite-time Koopman Identifier: A Unified Batch-online Learning Framework for Joint Learning of Koopman Structure and Parameters

Majid Mazouchi, Subramanya Nageshrao, Hamidreza Modares

T-Cal: An Optimal Test for the Calibration of Predictive Models

Donghwan Lee, Xinmeng Huang, Hamed Hassani, Edgar Dobriban

Dimensionality Reduction and Wasserstein Stability for Kernel Regression

Stephan Eckstein, Armin Iske, Mathias Trabs

Weisfeiler and Leman go Machine Learning: The Story so far

Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Kriege

Learning Conditional Generative Models for Phase Retrieval

Tobias Uelwer, Sebastian Konietzny, Alexander Oberstrass, Stefan Harmeling

Fair Data Representation for Machine Learning at the Pareto Frontier

Shizhou Xu, Thomas Strohmer

The Power of Contrast for Feature Learning: A Theoretical Analysis

Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou, Linjun Zhang

Mini-batching error and adaptive Langevin dynamics

Inass Sekkat, Gabriel Stoltz

Bandit problems with fidelity rewards

Gabor Lugosi, Ciara Pike-Burke, Pierre-Andre Savalle

Reproducing Kernels and New Approaches in Compositional Data Analysis

Binglin Li, Changwon Yoon, Jeongyoun Ahn

Benign Overfitting of Constant-Stepsize SGD for Linear Regression

Difan Zou, Jingfeng Wu, Vladimir braverman, Quanquan Gu, Sham M. Kakade

ProtoShotXAI: Using Prototypical Few-Shot Architecture for Explainable AI

Samuel Hess, Gregory Ditzler

Be More Active! Understanding the Differences Between Mean and Sampled Representations of Variational Autoencoders

Lisa Bonheme, Marek Grzes

Semi-Supervised Off-Policy Reinforcement Learning and Value Estimation for Dynamic Treatment Regimes

Aaron Sonabend-W, Nilanjana Laha, Ashwin N. Ananthakrishnan, Tianxi Cai, Rajarshi Mukherjee

Consistent Second-Order Conic Integer Programming for Learning Bayesian Networks

Simge Kucukyavuz, Ali Shojaie, Hasan Manzour, Linchuan Wei, Hao-Hsiang Wu

Scale Invariant Power Iteration

Cheolmin Kim, Youngseok Kim, Diego Klabjan

Higher-Order Spectral Clustering Under Superimposed Stochastic Block Models

Subhadeep Paul, Olgica Milenkovic, Yuguo Chen

Bagging in overparameterized learning: Risk characterization and risk monotonization

Pratik Patil, Jin-Hong Du, Arun Kumar Kuchibhotla

Operator learning with PCA-Net: upper and lower complexity bounds

Samuel Lanthaler

Mixed Regression via Approximate Message Passing

Nelvin Tan, Ramji Venkataramanan

The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima

Peter L. Bartlett, Philip M. Long, Olivier Bousquet

MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library

Siyi Hu, Yifan Zhong, Minquan Gao, Weixun Wang, Hao Dong

Fast Expectation Propagation for Heteroscedastic, Lasso-Penalized, and Quantile Regression

Jackson Zhou, John T. Ormerod, Clara Grazian

Zeroth-Order Alternating Gradient Descent Ascent Algorithms for A Class of Nonconvex-Nonconcave Minimax Problems

Zi Xu, Zi-Qi Wang, Jun-Lin Wang, Yu-Hong Dai

The Measure and Mismeasure of Fairness

Sam Corbett-Davies, Johann D. Gaebler, Hamed Nilforoshan, Ravi Shroff, Sharad Goel

Microcanonical Hamiltonian Monte Carlo

Jakob Robnik, G. Bruno De Luca, Eva Silverstein, Uros Seljak

Prediction Equilibrium for Dynamic Network Flows

Lukas Graf, Tobias Harks, Kostas Kollias, Michael Markl

Dimension Reduction and MARS

Yu Liu LIU, Degui Li, Yingcun Xia

Nevis'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research

Jörg Bornschein, Alexandre Galashov, Ross Hemsley, Amal Rannen-Triki, Yutian Chen

Fast Screening Rules for Optimal Design via Quadratic Lasso Reformulation

Guillaume Sagnol, Luc Pronzato

Multi-Consensus Decentralized Accelerated Gradient Descent

Haishan Ye, Luo Luo, Ziang Zhou, Tong Zhang

Continuous-in-time Limit for Bayesian Bandits

Yuhua Zhu, Zachary Izzo, Lexing Ying

Two Sample Testing in High Dimension via Maximum Mean Discrepancy

Hanjia Gao, Xiaofeng Shao

Random Feature Amplification: Feature Learning and Generalization in Neural Networks

Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett

Pivotal Estimation of Linear Discriminant Analysis in High Dimensions

Ethan X. Fang, Yajun Mei, Yuyang Shi, Qunzhi Xu, Tuo Zhao