JMLR 2021

290 papers

On Universal Approximation and Error Bounds for Fourier Neural Operators

Nikola Kovachki, Samuel Lanthaler, Siddhartha Mishra

VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning

Luisa Zintgraf, Sebastian Schulze, Cong Lu, Leo Feng, Maximilian Igl

A Theory of the Risk for Optimization with Relaxation and its Application to Support Vector Machines

Marco C. Campi, Simone Garatti

V-statistics and Variance Estimation

Zhengze Zhou, Lucas Mentch, Giles Hooker

An Online Sequential Test for Qualitative Treatment Effects

Chengchun Shi, Shikai Luo, Hongtu Zhu, Rui Song

Double Generative Adversarial Networks for Conditional Independence Testing

Chengchun Shi, Tianlin Xu, Wicher Bergsma, Lexin Li

Linear Bandits on Uniformly Convex Sets

Thomas Kerdreux, Christophe Roux, Alexandre D'aspremont, Sebastian Pokutta

Non-linear, Sparse Dimensionality Reduction via Path Lasso Penalized Autoencoders

Oskar Allerbo, Rebecka Jörnsten

LDLE: Low Distortion Local Eigenmaps

Dhruv Kohli, Alexander Cloninger, Gal Mishne

Contrastive Estimation Reveals Topic Posterior Information to Linear Models

Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu

Graph Matching with Partially-Correct Seeds

Liren Yu, Jiaming Xu, Xiaojun Lin

Fast Learning for Renewal Optimization in Online Task Scheduling

Michael J. Neely

Multilevel Monte Carlo Variational Inference

Masahiro Fujisawa, Issei Sato

Gaussian Approximation for Bias Reduction in Q-Learning

Carlo D'Eramo, Andrea Cini, Alessandro Nuara, Matteo Pirotta, Cesare Alippi

Estimating the Lasso's Effective Noise

Johannes Lederer, Michael Vogt

Partial Policy Iteration for L1-Robust Markov Decision Processes

Chin Pang Ho, Marek Petrik, Wolfram Wiesemann

Simultaneous Change Point Inference and Structure Recovery for High Dimensional Gaussian Graphical Models

Bin Liu, Xinsheng Zhang, Yufeng Liu

On the Hardness of Robust Classification

Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell

Transferability of Spectral Graph Convolutional Neural Networks

Ron Levie, Wei Huang, Lorenzo Bucci, Michael Bronstein, Gitta Kutyniok

Nonparametric Continuous Sensor Registration

William Clark, Maani Ghaffari, Anthony Bloch

Further results on latent discourse models and word embeddings

Sammy Khalife, Douglas Gonçalves, Youssef Allouah, Leo Liberti

CAT: Compression-Aware Training for bandwidth reduction

Chaim Baskin, Brian Chmiel, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein

Stable-Baselines3: Reliable Reinforcement Learning Implementations

Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus

Reproducing kernel Hilbert C*-module and kernel mean embeddings

Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Takeshi Katsura

Learning Bayesian Networks from Ordinal Data

Xiang Ge Luo, Giusi Moffa, Jack Kuipers

Exact Asymptotics for Linear Quadratic Adaptive Control

Feicheng Wang, Lucas Janson

Regularized spectral methods for clustering signed networks

Mihai Cucuringu, Apoorv Vikram Singh, Déborah Sulem, Hemant Tyagi

On the Stability Properties and the Optimization Landscape of Training Problems with Squared Loss for Neural Networks and General Nonlinear Conic Approximation Schemes

Constantin Christof

Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning

Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang

Domain adaptation under structural causal models

Yuansi Chen, Peter Bühlmann

Learning Strategies in Decentralized Matching Markets under Uncertain Preferences

Xiaowu Dai, Michael I. Jordan

ROOTS: Object-Centric Representation and Rendering of 3D Scenes

Chang Chen, Fei Deng, Sungjin Ahn

Optimized Score Transformation for Consistent Fair Classification

Dennis Wei, Karthikeyan Natesan Ramamurthy, Flavio P. Calmon

Estimating Uncertainty Intervals from Collaborating Networks

Tianhui Zhou, Yitong Li, Yuan Wu, David Carlson

Model Linkage Selection for Cooperative Learning

Jiaying Zhou, Jie Ding, Kean Ming Tan, Vahid Tarokh

Adversarial Monte Carlo Meta-Learning of Optimal Prediction Procedures

Alex Luedtke, Incheoul Chung, Oleg Sofrygin

Inference for the Case Probability in High-dimensional Logistic Regression

Zijian Guo, Prabrisha Rakshit, Daniel S. Herman, Jinbo Chen

Bifurcation Spiking Neural Network

Shao-Qun Zhang, Zhao-Yu Zhang, Zhi-Hua Zhou

Batch greedy maximization of non-submodular functions: Guarantees and applications to experimental design

Jayanth Jagalur-Mohan, Youssef Marzouk

Tractable Approximate Gaussian Inference for Bayesian Neural Networks

James-A. Goulet, Luong Ha Nguyen, Saeid Amiri

Bayesian time-aligned factor analysis of paired multivariate time series

Arkaprava Roy, Jana Schaich Borg, David B Dunson

On the Riemannian Search for Eigenvector Computation

Zhiqiang Xu, Ping Li

Statistically and Computationally Efficient Change Point Localization in Regression Settings

Daren Wang, Zifeng Zhao, Kevin Z. Lin, Rebecca Willett

Statistical Guarantees for Local Spectral Clustering on Random Neighborhood Graphs

Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani

Mixture Martingales Revisited with Applications to Sequential Tests and Confidence Intervals

Emilie Kaufmann, Wouter M. Koolen

On lp-hyperparameter Learning via Bilevel Nonsmooth Optimization

Takayuki Okuno, Akiko Takeda, Akihiro Kawana, Motokazu Watanabe

Consistency of Gaussian Process Regression in Metric Spaces

Peter Koepernik, Florian Pfaff

Quasi-Monte Carlo Quasi-Newton in Variational Bayes

Sifan Liu, Art B. Owen

Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations

Jesus Maria Sanz-Serna, Konstantinos C. Zygalakis

Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks

Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste

DIG: A Turnkey Library for Diving into Graph Deep Learning Research

Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan

Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo

Mert Gurbuzbalaban, Xuefeng GAO, Yuanhan Hu, Lingjiong Zhu

DeEPCA: Decentralized Exact PCA with Linear Convergence Rate

Haishan Ye, Tong Zhang

Consensus-Based Optimization on the Sphere: Convergence to Global Minimizers and Machine Learning

Massimo Fornasier, Lorenzo Pareschi, Hui Huang, Philippe Sünnen

Expanding Boundaries of Gap Safe Screening

Cassio F. Dantas, Emmanuel Soubies, Cédric Févotte

GIBBON: General-purpose Information-Based Bayesian Optimisation

Henry B. Moss, David S. Leslie, Javier Gonzalez, Paul Rayson

A general linear-time inference method for Gaussian Processes on one dimension

Jackson Loper, David Blei, John P. Cunningham, Liam Paninski

A Generalised Linear Model Framework for β-Variational Autoencoders based on Exponential Dispersion Families

Robert Sicks, Ralf Korn, Stefanie Schwaar

Probabilistic Iterative Methods for Linear Systems

Jon Cockayne, Ilse C.F. Ipsen, Chris J. Oates, Tim W. Reid

sklvq: Scikit Learning Vector Quantization

Rick van Veen, Michael Biehl, Gert-Jan de Vries

Learning with semi-definite programming: statistical bounds based on fixed point analysis and excess risk curvature

Stéphane Chrétien, Mihai Cucuringu, Guillaume Lecue, Lucie Neirac

Convolutional Neural Networks Are Not Invariant to Translation, but They Can Learn to Be

Valerio Biscione, Jeffrey S. Bowers

How Well Generative Adversarial Networks Learn Distributions

Tengyuan Liang

Tighter Risk Certificates for Neural Networks

Maria Perez-Ortiz, Omar Rivasplata, John Shawe-Taylor, Csaba Szepesvári

FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection

Yang Liu, Tao Fan, Tianjian Chen, Qian Xu, Qiang Yang

Representer Theorems in Banach Spaces: Minimum Norm Interpolation, Regularized Learning and Semi-Discrete Inverse Problems

Rui Wang, Yuesheng Xu

Bayesian Distance Clustering

Leo L. Duan, David B. Dunson

Stochastic Online Optimization using Kalman Recursion

Joseph de Vilmarest, Olivier Wintenberger

Classification vs regression in overparameterized regimes: Does the loss function matter?

Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel Hsu

A Bayes-Optimal View on Adversarial Examples

Eitan Richardson, Yair Weiss

Shape-Enforcing Operators for Generic Point and Interval Estimators of Functions

Xi Chen, Victor Chernozhukov, Ivan Fernandez-Val, Scott Kostyshak, Ye Luo

Soft Tensor Regression

Georgia Papadogeorgou, Zhengwu Zhang, David B. Dunson

Thompson Sampling Algorithms for Cascading Bandits

Zixin Zhong, Wang Chi Chueng, Vincent Y. F. Tan

A Unified Framework for Spectral Clustering in Sparse Graphs

Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay

Context-dependent Networks in Multivariate Time Series: Models, Methods, and Risk Bounds in High Dimensions

Lili Zheng, Garvesh Raskutti, Rebecca Willett, Benjamin Mark

TensorHive: Management of Exclusive GPU Access for Distributed Machine Learning Workloads

Paweł Rościszewski, Michał Martyniak, Filip Schodowski

dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python

Hubert Baniecki, Wojciech Kretowicz, Piotr Piątyszek, Jakub Wiśniewski, Przemysław Biecek

Cooperative SGD: A Unified Framework for the Design and Analysis of Local-Update SGD Algorithms

Jianyu Wang, Gauri Joshi

Convex Geometry and Duality of Over-parameterized Neural Networks

Tolga Ergen, Mert Pilanci

Bandit Learning in Decentralized Matching Markets

Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan

Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks

Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla

Explaining by Removing: A Unified Framework for Model Explanation

Ian Covert, Scott Lundberg, Su-in Lee

Oblivious Data for Fairness with Kernels

Steffen Grünewälder, Azadeh Khaleghi

A Unified Convergence Analysis for Shuffling-Type Gradient Methods

Lam M. Nguyen, Quoc Tran-Dinh, Dzung T. Phan, PHUONG HA NGUYEN, Marten van Dijk

Hamilton-Jacobi Deep Q-Learning for Deterministic Continuous-Time Systems with Lipschitz Continuous Controls

Jeongho Kim, Jaeuk Shin, Insoon Yang

Langevin Monte Carlo: random coordinate descent and variance reduction

ZHIYAN DING, Qin Li

Failures of Model-dependent Generalization Bounds for Least-norm Interpolation

Peter L. Bartlett, Philip M. Long

Learning partial correlation graphs and graphical models by covariance queries

Gabor Lugosi, Jakub Truszkowski, Vasiliki Velona, Piotr Zwiernik

Interpretable Deep Generative Recommendation Models

Huafeng Liu, Liping Jing, Jingxuan Wen, Pengyu Xu, Jiaqi Wang

Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization

Yingfan Wang, Haiyang Huang, Cynthia Rudin, Yaron Shaposhnik

Refined approachability algorithms and application to regret minimization with global costs

Joon Kwon

On ADMM in Deep Learning: Convergence and Saturation-Avoidance

Jinshan Zeng, Shao-Bo Lin, Yuan Yao, Ding-Xuan Zhou

Integrated Principal Components Analysis

Tiffany M. Tang, Genevera I. Allen

Particle-Gibbs Sampling for Bayesian Feature Allocation Models

Alexandre Bouchard-côté, Andrew Roth

COKE: Communication-Censored Decentralized Kernel Learning

Ping Xu, Yue Wang, Xiang Chen, Zhi Tian

Learning Laplacian Matrix from Graph Signals with Sparse Spectral Representation

Pierre Humbert, Batiste Le Bars, Laurent Oudre, Argyris Kalogeratos, Nicolas Vayatis

Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings

Keith D. Levin, Fred Roosta, Minh Tang, Michael W. Mahoney, Carey E. Priebe

Majid Noroozi, Marianna Pensky, Ramchandra Rimal

Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models

Fei Wang, Ling Zhou, Lu Tang, Peter X.K. Song

On the Estimation of Network Complexity: Dimension of Graphons

Yann ISSARTEL