JMLR 2023

401 papers

Learning Optimal Feedback Operators and their Sparse Polynomial Approximations

Karl Kunisch, Donato Vásquez-Varas, Daniel Walter

Sensitivity-Free Gradient Descent Algorithms

Ion Matei, Maksym Zhenirovskyy, Johan de Kleer, John Maxwell

A PDE approach for regret bounds under partial monitoring

Erhan Bayraktar, Ibrahim Ekren, Xin Zhang

A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning

Arrasy Rahman, Ignacio Carlucho, Niklas Hopner, Stefano V. Albrecht

Causal Bandits for Linear Structural Equation Models

Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer

High-Dimensional Inference for Generalized Linear Models with Hidden Confounding

Jing Ouyang, Kean Ming Tan, Gongjun Xu

Weibull Racing Survival Analysis with Competing Events, Left Truncation, and Time-Varying Covariates

Quan Zhang, Yanxun Xu, Mei-Cheng Wang, Mingyuan Zhou

Erratum: Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm

Louis-Philippe Vignault, Audrey Durand, Pascal Germain

Augmented Transfer Regression Learning with Semi-non-parametric Nuisance Models

Molei Liu, Yi Zhang, Katherine P. Liao, Tianxi Cai

From Understanding Genetic Drift to a Smart-Restart Mechanism for Estimation-of-Distribution Algorithms

Weijie Zheng, Benjamin Doerr

A Unified Analysis of Multi-task Functional Linear Regression Models with Manifold Constraint and Composite Quadratic Penalty

Shiyuan He, Hanxuan Ye, Kejun He

Deletion and Insertion Tests in Regression Models

Naofumi Hama, Masayoshi Mase, Art B. Owen

Deep Neural Networks with Dependent Weights: Gaussian Process Mixture Limit, Heavy Tails, Sparsity and Compressibility

Hoil Lee, Fadhel Ayed, Paul Jung, Juho Lee, Hongseok Yang

A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits

Yasin Abbasi-Yadkori, András György, Nevena Lazic

Universal Approximation Property of Invertible Neural Networks

Isao Ishikawa, Takeshi Teshima, Koichi Tojo, Kenta Oono, Masahiro Ikeda

Low Tree-Rank Bayesian Vector Autoregression Models

Leo L Duan, Zeyu Yuwen, George Michailidis, Zhengwu Zhang

Generic Unsupervised Optimization for a Latent Variable Model With Exponential Family Observables

Hamid Mousavi, Jakob Drefs, Florian Hirschberger, Jörg Lücke

A Complete Characterization of Linear Estimators for Offline Policy Evaluation

Juan C. Perdomo, Akshay Krishnamurthy, Peter Bartlett, Sham Kakade

Near-Optimal Weighted Matrix Completion

Oscar López

Community models for networks observed through edge nominations

Tianxi Li, Elizaveta Levina, Ji Zhu

The Bayesian Learning Rule

Mohammad Emtiyaz Khan, Håvard Rue

Removing Data Heterogeneity Influence Enhances Network Topology Dependence of Decentralized SGD

Kun Yuan, Sulaiman A. Alghunaim, Xinmeng Huang

Sparse Markov Models for High-dimensional Inference

Guilherme Ost, Daniel Y. Takahashi

Distinguishing Cause and Effect in Bivariate Structural Causal Models: A Systematic Investigation

Christoph Kading, Jakob Runge

Elastic Gradient Descent, an Iterative Optimization Method Approximating the Solution Paths of the Elastic Net

Oskar Allerbo, Johan Jonasson, Rebecka Jörnsten

On Biased Compression for Distributed Learning

Aleksandr Beznosikov, Samuel Horváth, Peter Richtarik, Mher Safaryan

Adaptive Clustering Using Kernel Density Estimators

Ingo Steinwart, Bharath K. Sriperumbudur, Philipp Thomann

A Continuous-time Stochastic Gradient Descent Method for Continuous Data

Kexin Jin, Jonas Latz, Chenguang Liu, Carola-Bibiane Schönlieb

Online Non-stochastic Control with Partial Feedback

Yu-Hu Yan, Peng Zhao, Zhi-Hua Zhou

Distributed Sparse Regression via Penalization

Yao Ji, Gesualdo Scutari, Ying Sun, Harsha Honnappa

Causal Discovery with Unobserved Confounding and Non-Gaussian Data

Y. Samuel Wang, Mathias Drton

Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation

Xiao-Tong Yuan, Ping Li

Dynamic Ranking with the BTL Model: A Nearest Neighbor based Rank Centrality Method

Eglantine Karlé, Hemant Tyagi

Revisiting minimum description length complexity in overparameterized models

Raaz Dwivedi, Chandan Singh, Bin Yu, Martin Wainwright

Sparse Plus Low Rank Matrix Decomposition: A Discrete Optimization Approach

Dimitris Bertsimas, Ryan Cory-Wright, Nicholas A. G. Johnson

On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression Estimators

Zejian Liu, Meng Li

Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction

Jue Hou, Zijian Guo, Tianxi Cai

ProtoryNet - Interpretable Text Classification Via Prototype Trajectories

Dat Hong, Tong Wang, Stephen Baek

Distributed Algorithms for U-statistics-based Empirical Risk Minimization

Lanjue Chen, Alan T.K. Wan, Shuyi Zhang, Yong Zhou

Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training?

Shuxiao Chen, Qinqing Zheng, Qi Long, Weijie J. Su

Nearest Neighbor Dirichlet Mixtures

Shounak Chattopadhyay, Antik Chakraborty, David B. Dunson

Learning to Rank under Multinomial Logit Choice

James A. Grant, David S. Leslie

Scalable high-dimensional Bayesian varying coefficient models with unknown within-subject covariance

Ray Bai, Mary R. Boland, Yong Chen

Multi-view Collaborative Gaussian Process Dynamical Systems

Shiliang Sun, Jingjing Fei, Jing Zhao, Liang Mao

Fairlearn: Assessing and Improving Fairness of AI Systems

Hilde Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz

Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks

Khurram Javed, Haseeb Shah, Richard S. Sutton, Martha White

Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architectures

Mike Heddes, Igor Nunes, Pere Vergés, Denis Kleyko, Danny Abraham

skrl: Modular and Flexible Library for Reinforcement Learning

Antonio Serrano-Muñoz, Dimitrios Chrysostomou, Simon Bøgh, Nestor Arana-Arexolaleiba

Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model

Alexandra Sasha Luccioni, Sylvain Viguier, Anne-Laure Ligozat

Adaptive False Discovery Rate Control with Privacy Guarantee

Xintao Xia, Zhanrui Cai

Atlas: Few-shot Learning with Retrieval Augmented Language Models

Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni

Convex Reinforcement Learning in Finite Trials

Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli

Unbiased Multilevel Monte Carlo Methods for Intractable Distributions: MLMC Meets MCMC

Tianze Wang, Guanyang Wang

Improving multiple-try Metropolis with local balancing

Philippe Gagnon, Florian Maire, Giacomo Zanella

Importance Sparsification for Sinkhorn Algorithm

Mengyu Li, Jun Yu, Tao Li, Cheng Meng

Graph Attention Retrospective

Kimon Fountoulakis, Amit Levi, Shenghao Yang, Aseem Baranwal, Aukosh Jagannath

Confidence Intervals and Hypothesis Testing for High-dimensional Quantile Regression: Convolution Smoothing and Debiasing

Yibo Yan, Xiaozhou Wang, Riquan Zhang

Selection by Prediction with Conformal p-values

Ying Jin, Emmanuel J. Candès

Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics

Kamélia Daudel, Joe Benton, Yuyang Shi, Arnaud Doucet

Sparse Graph Learning from Spatiotemporal Time Series

Andrea Cini, Daniele Zambon, Cesare Alippi

Improved Powered Stochastic Optimization Algorithms for Large-Scale Machine Learning

Zhuang Yang

PaLM: Scaling Language Modeling with Pathways

Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra

Leaky Hockey Stick Loss: The First Negatively Divergent Margin-based Loss Function for Classification

Oh-Ran Kwon, Hui Zou

Efficient Computation of Rankings from Pairwise Comparisons

M. E. J. Newman

Scalable Computation of Causal Bounds

Madhumitha Shridharan, Garud Iyengar

Neural Q-learning for solving PDEs

Samuel N. Cohen, Deqing Jiang, Justin Sirignano

Tractable and Near-Optimal Adversarial Algorithms for Robust Estimation in Contaminated Gaussian Models

Ziyue Wang, Zhiqiang Tan

MultiZoo and MultiBench: A Standardized Toolkit for Multimodal Deep Learning

Paul Pu Liang, Yiwei Lyu, Xiang Fan, Arav Agarwal, Yun Cheng

Strategic Knowledge Transfer

Max Olan Smith, Thomas Anthony, Michael P. Wellman

Lifted Bregman Training of Neural Networks

Xiaoyu Wang, Martin Benning

Statistical Comparisons of Classifiers by Generalized Stochastic Dominance

Christoph Jansen, Malte Nalenz, Georg Schollmeyer, Thomas Augustin

Sample Complexity for Distributionally Robust Learning under chi-square divergence

Zhengyu Zhou, Weiwei Liu

Interpretable and Fair Boolean Rule Sets via Column Generation

Connor Lawless, Sanjeeb Dash, Oktay Gunluk, Dennis Wei

On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure

Yunhua Xiang, Tianyu Zhang, Xu Wang, Ali Shojaie, Noah Simon

Autoregressive Networks

Binyan Jiang, Jialiang Li, Qiwei Yao

Merlion: End-to-End Machine Learning for Time Series

Aadyot Bhatnagar, Paul Kassianik, Chenghao Liu, Tian Lan, Wenzhuo Yang

Limits of Dense Simplicial Complexes

T. Mitchell Roddenberry, Santiago Segarra

RankSEG: A Consistent Ranking-based Framework for Segmentation

Ben Dai, Chunlin Li

Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data

Bingqing Hu, Bin Nan

Single Timescale Actor-Critic Method to Solve the Linear Quadratic Regulator with Convergence Guarantees

Mo Zhou, Jianfeng Lu

Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices

Doudou Zhou, Tianxi Cai, Junwei Lu

A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning

Wei-Fang Sun, Cheng-Kuang Lee, Simon See, Chun-Yi Lee

Functional L-Optimality Subsampling for Functional Generalized Linear Models with Massive Data

Hua Liu, Jinhong You, Jiguo Cao

Adaptation Augmented Model-based Policy Optimization

Jian Shen, Hang Lai, Minghuan Liu, Han Zhao, Yong Yu

GANs as Gradient Flows that Converge

Yu-Jui Huang, Yuchong Zhang

Random Forests for Change Point Detection

Malte Londschien, Peter Bühlmann, Solt Kovács

Least Squares Model Averaging for Distributed Data

Haili Zhang, Zhaobo Liu, Guohua Zou

An Empirical Investigation of the Role of Pre-training in Lifelong Learning

Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar, Emma Strubell

Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications

Marcel Wienöbst, Max Bannach, Maciej Liśkiewicz

An Inexact Augmented Lagrangian Algorithm for Training Leaky ReLU Neural Network with Group Sparsity

Wei Liu, Xin Liu, Xiaojun Chen

Entropic Fictitious Play for Mean Field Optimization Problem

Fan Chen, Zhenjie Ren, Songbo Wang

GFlowNet Foundations

Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J. Hu, Mo Tiwari

LibMTL: A Python Library for Deep Multi-Task Learning

Baijiong Lin, Yu Zhang

Minimax Risk Classifiers with 0-1 Loss

Santiago Mazuelas, Mauricio Romero, Peter Grünwald

Augmented Sparsifiers for Generalized Hypergraph Cuts

Nate Veldt, Austin R. Benson, Jon Kleinberg

Non-stationary Online Learning with Memory and Non-stochastic Control

Peng Zhao, Yu-Hu Yan, Yu-Xiang Wang, Zhi-Hua Zhou

L0Learn: A Scalable Package for Sparse Learning using L0 Regularization

Hussein Hazimeh, Rahul Mazumder, Tim Nonet

Buffered Asynchronous SGD for Byzantine Learning

Yi-Rui Yang, Wu-jun Li

A Non-parametric View of FedAvg and FedProx:Beyond Stationary Points

Lili Su, Jiaming Xu, Pengkun Yang

Multiplayer Performative Prediction: Learning in Decision-Dependent Games

Adhyyan Narang, Evan Faulkner, Dmitriy Drusvyatskiy, Maryam Fazel, Lillian J. Ratliff