JMLR 2021

272 papers

A Bayes-Optimal View on Adversarial Examples Eitan Richardson, Yair Weiss

A Bayesian Contiguous Partitioning Method for Learning Clustered Latent Variables Zhao Tang Luo, Huiyan Sang, Bani Mallick

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A Contextual Bandit Bake-Off Alberto Bietti, Alekh Agarwal, John Langford

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A Distributed Method for Fitting Laplacian Regularized Stratified Models Jonathan Tuck, Shane Barratt, Stephen Boyd

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A Fast Globally Linearly Convergent Algorithm for the Computation of Wasserstein Barycenters Lei Yang, Jia Li, Defeng Sun, Kim-Chuan Toh

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A Flexible Model-Free Prediction-Based Framework for Feature Ranking Jingyi Jessica Li, Yiling Elaine Chen, Xin Tong

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A General Framework for Adversarial Label Learning Chidubem Arachie, Bert Huang

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A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family Trambak Banerjee, Qiang Liu, Gourab Mukherjee, Wengunag Sun

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A General Linear-Time Inference Method for Gaussian Processes on One Dimension Jackson Loper, David Blei, John P. Cunningham, Liam Paninski

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A Generalised Linear Model Framework for Β-Variational Autoencoders Based on Exponential Dispersion Families Robert Sicks, Ralf Korn, Stefanie Schwaar

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A Greedy Algorithm for Quantizing Neural Networks Eric Lybrand, Rayan Saab

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A Lyapunov Analysis of Accelerated Methods in Optimization Ashia C. Wilson, Ben Recht, Michael I. Jordan

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A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo, Jan Peters, Joni Pajarinen

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A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms Oliver Kroemer, Scott Niekum, George Konidaris

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A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration Yuetian Luo, Garvesh Raskutti, Ming Yuan, Anru R. Zhang

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A Theory of the Risk for Optimization with Relaxation and Its Application to Support Vector Machines Marco C. Campi, Simone Garatti

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A Two-Level Decomposition Framework Exploiting First and Second Order Information for SVM Training Problems Giulio Galvan, Matteo Lapucci, Chih-Jen Lin, Marco Sciandrone

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A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints Guodong Zhang, Xuchan Bao, Laurent Lessard, Roger Grosse

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A Unified Convergence Analysis for Shuffling-Type Gradient Methods Lam M. Nguyen, Quoc Tran-Dinh, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk

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A Unified Framework for Random Forest Prediction Error Estimation Benjamin Lu, Johanna Hardin

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A Unified Framework for Spectral Clustering in Sparse Graphs Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay

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A Unified Sample Selection Framework for Output Noise Filtering: An Error-Bound Perspective Gaoxia Jiang, Wenjian Wang, Yuhua Qian, Jiye Liang

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Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent Tian Tong, Cong Ma, Yuejie Chi

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Achieving Fairness in the Stochastic Multi-Armed Bandit Problem Vishakha Patil, Ganesh Ghalme, Vineet Nair, Y. Narahari

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Adaptive Estimation of Nonparametric Functionals Lin Liu, Rajarshi Mukherjee, James M. Robins, Eric Tchetgen Tchetgen

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Adversarial Monte Carlo Meta-Learning of Optimal Prediction Procedures Alex Luedtke, Incheoul Chung, Oleg Sofrygin

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Aggregated Hold-Out Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle

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An Algorithmic View of L2 Regularization and Some Path-Following Algorithms Yunzhang Zhu, Renxiong Liu

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An Empirical Study of Bayesian Optimization: Acquisition Versus Partition Erich Merrill, Alan Fern, Xiaoli Fern, Nima Dolatnia

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An Importance Weighted Feature Selection Stability Measure Victor Hamer, Pierre Dupont

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An Inertial Newton Algorithm for Deep Learning Camille Castera, Jérôme Bolte, Cédric Févotte, Edouard Pauwels

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An Online Sequential Test for Qualitative Treatment Effects Chengchun Shi, Shikai Luo, Hongtu Zhu, Rui Song

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Analysis of High-Dimensional Continuous Time Markov Chains Using the Local Bouncy Particle Sampler Tingting Zhao, Alexandre Bouchard-Côté

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Analyzing the Discrepancy Principle for Kernelized Spectral Filter Learning Algorithms Alain Celisse, Martin Wahl

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Approximate Newton Methods Haishan Ye, Luo Luo, Zhihua Zhang

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Are We Forgetting About Compositional Optimisers in Bayesian Optimisation? Antoine Grosnit, Alexander I. Cowen-Rivers, Rasul Tutunov, Ryan-Rhys Griffiths, Jun Wang, Haitham Bou-Ammar

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As You like It: Localization via Paired Comparisons Andrew K. Massimino, Mark A. Davenport

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Asymptotic Normality, Concentration, and Coverage of Generalized Posteriors Jeffrey W. Miller

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Asynchronous Online Testing of Multiple Hypotheses Tijana Zrnic, Aaditya Ramdas, Michael I. Jordan

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Attention Is Turing-Complete Jorge Pérez, Pablo Barceló, Javier Marinkovic

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Banach Space Representer Theorems for Neural Networks and Ridge Splines Rahul Parhi, Robert D. Nowak

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Bandit Convex Optimization in Non-Stationary Environments Peng Zhao, Guanghui Wang, Lijun Zhang, Zhi-Hua Zhou

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Bandit Learning in Decentralized Matching Markets Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan

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Batch Greedy Maximization of Non-Submodular Functions: Guarantees and Applications to Experimental Design Jayanth Jagalur-Mohan, Youssef Marzouk

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Bayesian Distance Clustering Leo L. Duan, David B. Dunson

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Bayesian Text Classification and Summarization via a Class-Specified Topic Model Feifei Wang, Junni L. Zhang, Yichao Li, Ke Deng, Jun S. Liu

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Bayesian Time-Aligned Factor Analysis of Paired Multivariate Time Series Arkaprava Roy, Jana Schaich Borg, David B Dunson

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Benchmarking Unsupervised Object Representations for Video Sequences Marissa A. Weis, Kashyap Chitta, Yash Sharma, Wieland Brendel, Matthias Bethge, Andreas Geiger, Alexander S. Ecker

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Beyond English-Centric Multilingual Machine Translation Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Michael Auli, Armand Joulin

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Bifurcation Spiking Neural Network Shao-Qun Zhang, Zhao-Yu Zhang, Zhi-Hua Zhou

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Black-Box Reductions for Zeroth-Order Gradient Algorithms to Achieve Lower Query Complexity Bin Gu, Xiyuan Wei, Shangqian Gao, Ziran Xiong, Cheng Deng, Heng Huang

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CAT: Compression-Aware Training for Bandwidth Reduction Chaim Baskin, Brian Chmiel, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, Avi Mendelson

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Classification vs Regression in Overparameterized Regimes: Does the Loss Function Matter? Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel Hsu, Anant Sahai

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COKE: Communication-Censored Decentralized Kernel Learning Ping Xu, Yue Wang, Xiang Chen, Zhi Tian

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Collusion Detection and Ground Truth Inference in Crowdsourcing for Labeling Tasks Changyue Song, Kaibo Liu, Xi Zhang

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Communication-Efficient Distributed Covariance Sketch, with Application to Distributed PCA Zengfeng Huang, Xuemin Lin, Wenjie Zhang, Ying Zhang

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Conditional Independences and Causal Relations Implied by Sets of Equations Tineke Blom, Mirthe M. van Diepen, Joris M. Mooij

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Consensus-Based Optimization on the Sphere: Convergence to Global Minimizers and Machine Learning Massimo Fornasier, Lorenzo Pareschi, Hui Huang, Philippe Sünnen

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Consistency of Gaussian Process Regression in Metric Spaces Peter Koepernik, Florian Pfaff

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Consistent Estimation of Small Masses in Feature Sampling Fadhel Ayed, Marco Battiston, Federico Camerlenghi, Stefano Favaro

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Consistent Semi-Supervised Graph Regularization for High Dimensional Data Xiaoyi Mai, Romain Couillet

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Context-Dependent Networks in Multivariate Time Series: Models, Methods, and Risk Bounds in High Dimensions Lili Zheng, Garvesh Raskutti, Rebecca Willett, Benjamin Mark

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Continuous Time Analysis of Momentum Methods Nikola B. Kovachki, Andrew M. Stuart

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Contrastive Estimation Reveals Topic Posterior Information to Linear Models Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu

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Convergence Guarantees for Gaussian Process Means with Misspecified Likelihoods and Smoothness George Wynne, François-Xavier Briol, Mark Girolami

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Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm Defeng Sun, Kim-Chuan Toh, Yancheng Yuan

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Convex Geometry and Duality of Over-Parameterized Neural Networks Tolga Ergen, Mert Pilanci

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Convolutional Neural Networks Are Not Invariant to Translation, but They Can Learn to Be Valerio Biscione, Jeffrey S. Bowers

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Cooperative SGD: A Unified Framework for the Design and Analysis of Local-Update SGD Algorithms Jianyu Wang, Gauri Joshi

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Counterfactual Mean Embeddings Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, Sanparith Marukatat

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Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo Mert Gürbüzbalaban, Xuefeng Gao, Yuanhan Hu, Lingjiong Zhu

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DeEPCA: Decentralized Exact PCA with Linear Convergence Rate Haishan Ye, Tong Zhang

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Determining the Number of Communities in Degree-Corrected Stochastic Block Models Shujie Ma, Liangjun Su, Yichong Zhang

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Differentially Private Regression and Classification with Sparse Gaussian Processes Michael Thomas Smith, Mauricio A. Alvarez, Neil D. Lawrence

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Domain Adaptation Under Structural Causal Models Yuansi Chen, Peter Bühlmann

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Domain Generalization by Marginal Transfer Learning Gilles Blanchard, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, Clayton Scott

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Double Generative Adversarial Networks for Conditional Independence Testing Chengchun Shi, Tianlin Xu, Wicher Bergsma, Lexin Li

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Doubly Infinite Residual Neural Networks: A Diffusion Process Approach Stefano Peluchetti, Stefano Favaro

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Dynamic Tensor Recommender Systems Yanqing Zhang, Xuan Bi, Niansheng Tang, Annie Qu

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Edge Sampling Using Local Network Information Can M. Le

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Empirical Bayes Matrix Factorization Wei Wang, Matthew Stephens

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Entangled Kernels - Beyond Separability Riikka Huusari, Hachem Kadri

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Estimating the Lasso's Effective Noise Johannes Lederer, Michael Vogt

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Estimating Uncertainty Intervals from Collaborating Networks Tianhui Zhou, Yitong Li, Yuan Wu, David Carlson

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Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing Approach Zhe Fei, Yi Li

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Estimation and Optimization of Composite Outcomes Daniel J. Luckett, Eric B. Laber, Siyeon Kim, Michael R. Kosorok

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Exact Asymptotics for Linear Quadratic Adaptive Control Feicheng Wang, Lucas Janson

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Expanding Boundaries of Gap Safe Screening Cassio F. Dantas, Emmanuel Soubies, Cédric Févotte

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Explaining by Removing: A Unified Framework for Model Explanation Ian Covert, Scott Lundberg, Su-In Lee

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Explaining Explanations: Axiomatic Feature Interactions for Deep Networks Joseph D. Janizek, Pascal Sturmfels, Su-In Lee

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Factorization Machines with Regularization for Sparse Feature Interactions Kyohei Atarashi, Satoshi Oyama, Masahito Kurihara

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Failures of Model-Dependent Generalization Bounds for Least-Norm Interpolation Peter L. Bartlett, Philip M. Long

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Fast Learning for Renewal Optimization in Online Task Scheduling Michael J. Neely

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Finite Time LTI System Identification Tuhin Sarkar, Alexander Rakhlin, Munther A. Dahleh

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Finite-Sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime Niladri S. Chatterji, Philip M. Long

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First-Order Convergence Theory for Weakly-Convex-Weakly-Concave Min-Max Problems Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang

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FLAME: A Fast Large-Scale Almost Matching Exactly Approach to Causal Inference Tianyu Wang, Marco Morucci, M. Usaid Awan, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

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Flexible Signal Denoising via Flexible Empirical Bayes Shrinkage Zhengrong Xing, Peter Carbonetto, Matthew Stephens

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From Fourier to Koopman: Spectral Methods for Long-Term Time Series Prediction Henning Lange, Steven L. Brunton, J. Nathan Kutz

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From Low Probability to High Confidence in Stochastic Convex Optimization Damek Davis, Dmitriy Drusvyatskiy, Lin Xiao, Junyu Zhang

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Further Results on Latent Discourse Models and Word Embeddings Sammy Khalife, Douglas Gonçalves, Youssef Allouah, Leo Liberti

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Gaussian Approximation for Bias Reduction in Q-Learning Carlo D'Eramo, Andrea Cini, Alessandro Nuara, Matteo Pirotta, Cesare Alippi, Jan Peters, Marcello Restelli

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GemBag: Group Estimation of Multiple Bayesian Graphical Models Xinming Yang, Lingrui Gan, Naveen N. Narisetty, Feng Liang

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Generalization Performance of Multi-Pass Stochastic Gradient Descent with Convex Loss Functions Yunwen Lei, Ting Hu, Ke Tang

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Generalization Properties of Hyper-RKHS and Its Applications Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A.K. Suykens

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Geometric Structure of Graph Laplacian Embeddings Nicolás García Trillos, Franca Hoffmann, Bamdad Hosseini

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GIBBON: General-Purpose Information-Based Bayesian Optimisation Henry B. Moss, David S. Leslie, Javier Gonzalez, Paul Rayson

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Global and Quadratic Convergence of Newton Hard-Thresholding Pursuit Shenglong Zhou, Naihua Xiu, Hou-Duo Qi

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Gradient Methods Never Overfit on Separable Data Ohad Shamir

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Graph Matching with Partially-Correct Seeds Liren Yu, Jiaming Xu, Xiaojun Lin

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Guided Visual Exploration of Relations in Data Sets Kai Puolamäki, Emilia Oikarinen, Andreas Henelius

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Hamilton-Jacobi Deep Q-Learning for Deterministic Continuous-Time Systems with Lipschitz Continuous Controls Jeongho Kim, Jaeuk Shin, Insoon Yang

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Hardness of Identity Testing for Restricted Boltzmann Machines and Potts Models Antonio Blanca, Zongchen Chen, Daniel Štefankovič, Eric Vigoda

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High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan

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Histogram Transform Ensembles for Large-Scale Regression Hanyuan Hang, Zhouchen Lin, Xiaoyu Liu, Hongwei Wen

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Hoeffding's Inequality for General Markov Chains and Its Applications to Statistical Learning Jianqing Fan, Bai Jiang, Qiang Sun

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Homogeneity Structure Learning in Large-Scale Panel Data with Heavy-Tailed Errors Di Xiao, Yuan Ke, Runze Li

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How to Gain on Power: Novel Conditional Independence Tests Based on Short Expansion of Conditional Mutual Information Mariusz Kubkowski, Jan Mielniczuk, Paweł Teisseyre

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How Well Generative Adversarial Networks Learn Distributions Tengyuan Liang

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Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-Box Model Tong Wang, Qihang Lin

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Hyperparameter Optimization via Sequential Uniform Designs Zebin Yang, Aijun Zhang

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Implicit Langevin Algorithms for Sampling from Log-Concave Densities Liam Hodgkinson, Robert Salomone, Fred Roosta

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Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning Charles H. Martin, Michael W. Mahoney

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Improved Shrinkage Prediction Under a Spiked Covariance Structure Trambak Banerjee, Gourab Mukherjee, Debashis Paul

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Improving Reproducibility in Machine Learning Research(A Report from the NeurIPS 2019 Reproducibility Program) Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Lariviere, Alina Beygelzimer, Florence d'Alche-Buc, Emily Fox, Hugo Larochelle

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Incorporating Unlabeled Data into Distributionally Robust Learning Charlie Frogner, Sebastian Claici, Edward Chien, Justin Solomon

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Individual Fairness in Hindsight Swati Gupta, Vijay Kamble

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Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace Jesús Arroyo, Avanti Athreya, Joshua Cape, Guodong Chen, Carey E. Priebe, Joshua T. Vogelstein

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Inference for the Case Probability in High-Dimensional Logistic Regression Zijian Guo, Prabrisha Rakshit, Daniel S. Herman, Jinbo Chen

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Inference in High-Dimensional Single-Index Models Under Symmetric Designs Hamid Eftekhari, Moulinath Banerjee, Ya'acov Ritov

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Information Criteria for Non-Normalized Models Takeru Matsuda, Masatoshi Uehara, Aapo Hyvarinen

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Integrated Principal Components Analysis Tiffany M. Tang, Genevera I. Allen

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Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data Minjie Wang, Genevera I. Allen

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Integrative High Dimensional Multiple Testing with Heterogeneity Under Data Sharing Constraints Molei Liu, Yin Xia, Kelly Cho, Tianxi Cai

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Interpretable Deep Generative Recommendation Models Huafeng Liu, Liping Jing, Jingxuan Wen, Pengyu Xu, Jiaqi Wang, Jian Yu, Michael K. Ng

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Is SGD a Bayesian Sampler? Well, Almost Chris Mingard, Guillermo Valle-Pérez, Joar Skalse, Ard A. Louis

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Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data Yikun Zhang, Yen-Chi Chen

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Knowing What You Know: Valid and Validated Confidence Sets in Multiclass and Multilabel Prediction Maxime Cauchois, Suyash Gupta, John C. Duchi

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L-SVRG and L-Katyusha with Arbitrary Sampling Xun Qian, Zheng Qu, Peter Richtárik

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Langevin Dynamics for Adaptive Inverse Reinforcement Learning of Stochastic Gradient Algorithms Vikram Krishnamurthy, George Yin

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Langevin Monte Carlo: Random Coordinate Descent and Variance Reduction Zhiyan Ding, Qin Li

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LassoNet: A Neural Network with Feature Sparsity Ismael Lemhadri, Feng Ruan, Louis Abraham, Robert Tibshirani

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LDLE: Low Distortion Local Eigenmaps Dhruv Kohli, Alexander Cloninger, Gal Mishne

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Learning a High-Dimensional Linear Structural Equation Model via L1-Regularized Regression Gunwoong Park, Sang Jun Moon, Sion Park, Jong-June Jeon

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Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation Melkior Ornik, Ufuk Topcu

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Learning Bayesian Networks from Ordinal Data Xiang Ge Luo, Giusi Moffa, Jack Kuipers

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Learning Interaction Kernels in Heterogeneous Systems of Agents from Multiple Trajectories Fei Lu, Mauro Maggioni, Sui Tang

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Learning Laplacian Matrix from Graph Signals with Sparse Spectral Representation Pierre Humbert, Batiste Le Bars, Laurent Oudre, Argyris Kalogeratos, Nicolas Vayatis

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Learning Partial Correlation Graphs and Graphical Models by Covariance Queries Gábor Lugosi, Jakub Truszkowski, Vasiliki Velona, Piotr Zwiernik

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Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives Antoine Dedieu, Hussein Hazimeh, Rahul Mazumder

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Learning Strategies in Decentralized Matching Markets Under Uncertain Preferences Xiaowu Dai, Michael I. Jordan

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Learning Whenever Learning Is Possible: Universal Learning Under General Stochastic Processes Steve Hanneke

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Learning with Semi-Definite Programming: Statistical Bounds Based on Fixed Point Analysis and Excess Risk Curvature Stéphane Chrétien, Mihai Cucuringu, Guillaume Lecué, Lucie Neirac

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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

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Linear Bandits on Uniformly Convex Sets Thomas Kerdreux, Christophe Roux, Alexandre d'Aspremont, Sebastian Pokutta

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LocalGAN: Modeling Local Distributions for Adversarial Response Generation Baoxun Wang, Zhen Xu, Huan Zhang, Kexin Qiu, Deyuan Zhang, Chengjie Sun

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Locally Differentially-Private Randomized Response for Discrete Distribution Learning Adriano Pastore, Michael Gastpar

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Locally Private K-Means Clustering Uri Stemmer

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Matrix Product States for Inference in Discrete Probabilistic Models Rasmus Bonnevie, Mikkel N. Schmidt

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MetaGrad: Adaptation Using Multiple Learning Rates in Online Learning Tim van Erven, Wouter M. Koolen, Dirk van der Hoeven

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Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models Fei Wang, Ling Zhou, Lu Tang, Peter X.K. Song

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Mixing Time of Metropolis-Hastings for Bayesian Community Detection Bumeng Zhuo, Chao Gao

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Mixture Martingales Revisited with Applications to Sequential Tests and Confidence Intervals Emilie Kaufmann, Wouter M. Koolen

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Mode-Wise Tensor Decompositions: Multi-Dimensional Generalizations of CUR Decompositions HanQin Cai, Keaton Hamm, Longxiu Huang, Deanna Needell

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Model Linkage Selection for Cooperative Learning Jiaying Zhou, Jie Ding, Kean Ming Tan, Vahid Tarokh

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Multi-Class Gaussian Process Classification with Noisy Inputs Carlos Villacampa-Calvo, Bryan Zaldívar, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato

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Multi-View Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis Andreas Damianou, Neil D. Lawrence, Carl Henrik Ek

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Multilevel Monte Carlo Variational Inference Masahiro Fujisawa, Issei Sato

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Neighborhood Structure Assisted Non-Negative Matrix Factorization and Its Application in Unsupervised Point-Wise Anomaly Detection Imtiaz Ahmed, Xia Ben Hu, Mithun P. Acharya, Yu Ding

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NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation Anastasis Kratsios, Cody Hyndman

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Non-Attracting Regions of Local Minima in Deep and Wide Neural Networks Henning Petzka, Cristian Sminchisescu

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Non-Linear, Sparse Dimensionality Reduction via Path Lasso Penalized Autoencoders Oskar Allerbo, Rebecka Jörnsten

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Non-Parametric Quantile Regression via the K-NN Fused Lasso Steven Siwei Ye, Oscar Hernan Madrid Padilla

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Nonparametric Continuous Sensor Registration William Clark, Maani Ghaffari, Anthony Bloch

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Nonparametric Modeling of Higher-Order Interactions via Hypergraphons Krishnakumar Balasubramanian

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Normalizing Flows for Probabilistic Modeling and Inference George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan

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NUQSGD: Provably Communication-Efficient Data-Parallel SGD via Nonuniform Quantization Ali Ramezani-Kebrya, Fartash Faghri, Ilya Markov, Vitalii Aksenov, Dan Alistarh, Daniel M. Roy

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Oblivious Data for Fairness with Kernels Steffen Grünewälder, Azadeh Khaleghi

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On ADMM in Deep Learning: Convergence and Saturation-Avoidance Jinshan Zeng, Shao-Bo Lin, Yuan Yao, Ding-Xuan Zhou

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On Efficient Multilevel Clustering via Wasserstein Distances Viet Huynh, Nhat Ho, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, Dinh Phung

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On Lp-Hyperparameter Learning via Bilevel Nonsmooth Optimization Takayuki Okuno, Akiko Takeda, Akihiro Kawana, Motokazu Watanabe

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On Multi-Armed Bandit Designs for Dose-Finding Trials Maryam Aziz, Emilie Kaufmann, Marie-Karelle Riviere

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On Solving Probabilistic Linear Diophantine Equations Patrick Kreitzberg, Oliver Serang

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On the Estimation of Network Complexity: Dimension of Graphons Yann Issartel

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On the Hardness of Robust Classification Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell

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On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests Krishnakumar Balasubramanian, Tong Li, Ming Yuan

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On the Riemannian Search for Eigenvector Computation Zhiqiang Xu, Ping Li

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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

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On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan

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On Universal Approximation and Error Bounds for Fourier Neural Operators Nikola Kovachki, Samuel Lanthaler, Siddhartha Mishra

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One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them Saber Salehkaleybar, Arsalan Sharifnassab, S. Jamaloddin Golestani

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Online Stochastic Gradient Descent on Non-Convex Losses from High-Dimensional Inference Gerard Ben Arous, Reza Gheissari, Aukosh Jagannath

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Optimal Bounds Between F-Divergences and Integral Probability Metrics Rohit Agrawal, Thibaut Horel

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Optimal Feedback Law Recovery by Gradient-Augmented Sparse Polynomial Regression Behzad Azmi, Dante Kalise, Karl Kunisch

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Optimal Minimax Variable Selection for Large-Scale Matrix Linear Regression Model Meiling Hao, Lianqiang Qu, Dehan Kong, Liuquan Sun, Hongtu Zhu

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Optimal Rates of Distributed Regression with Imperfect Kernels Hongwei Sun, Qiang Wu

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Optimal Structured Principal Subspace Estimation: Metric Entropy and Minimax Rates Tony Cai, Hongzhe Li, Rong Ma

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Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives Michael Muehlebach, Michael I. Jordan

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Optimized Score Transformation for Consistent Fair Classification Dennis Wei, Karthikeyan Natesan Ramamurthy, Flavio P. Calmon

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Partial Policy Iteration for L1-Robust Markov Decision Processes Chin Pang Ho, Marek Petrik, Wolfram Wiesemann

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Particle-Gibbs Sampling for Bayesian Feature Allocation Models Alexandre Bouchard-Côté, Andrew Roth

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Path Length Bounds for Gradient Descent and Flow Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas

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Pathwise Conditioning of Gaussian Processes James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth

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PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review Ivan Stelmakh, Nihar Shah, Aarti Singh

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Phase Diagram for Two-Layer ReLU Neural Networks at Infinite-Width Limit Tao Luo, Zhi-Qin John Xu, Zheng Ma, Yaoyu Zhang

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Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla

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Prediction Against a Limited Adversary Erhan Bayraktar, Ibrahim Ekren, Xin Zhang

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Prediction Under Latent Factor Regression: Adaptive PCR, Interpolating Predictors and Beyond Xin Bing, Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp

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Predictive Learning on Hidden Tree-Structured Ising Models Konstantinos E. Nikolakakis, Dionysios S. Kalogerias, Anand D. Sarwate

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Preference-Based Online Learning with Dueling Bandits: A Survey Viktor Bengs, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, Eyke Hüllermeier

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Probabilistic Iterative Methods for Linear Systems Jon Cockayne, Ilse C.F. Ipsen, Chris J. Oates, Tim W. Reid

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Projection-Free Decentralized Online Learning for Submodular Maximization over Time-Varying Networks Junlong Zhu, Qingtao Wu, Mingchuan Zhang, Ruijuan Zheng, Keqin Li

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Pseudo-Marginal Hamiltonian Monte Carlo Johan Alenlöv, Arnoud Doucet, Fredrik Lindsten

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PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Sahand Sharifzadeh, Volker Tresp, Jens Lehmann

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Quasi-Monte Carlo Quasi-Newton in Variational Bayes Sifan Liu, Art B. Owen

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Ranking and Synchronization from Pairwise Measurements via SVD Alexandre d'Aspremont, Mihai Cucuringu, Hemant Tyagi

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RaSE: Random Subspace Ensemble Classification Ye Tian, Yang Feng

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Refined Approachability Algorithms and Application to Regret Minimization with Global Costs Joon Kwon

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Regularized Spectral Methods for Clustering Signed Networks Mihai Cucuringu, Apoorv Vikram Singh, Déborah Sulem, Hemant Tyagi

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Regulating Greed over Time in Multi-Armed Bandits Stefano Tracà, Cynthia Rudin, Weiyu Yan

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Replica Exchange for Non-Convex Optimization Jing Dong, Xin T. Tong

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

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Reproducing Kernel Hilbert C*-Module and Kernel Mean Embeddings Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Takeshi Katsura, Yoshinobu Kawahara

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Residual Energy-Based Models for Text Anton Bakhtin, Yuntian Deng, Sam Gross, Myle Ott, Marc'Aurelio Ranzato, Arthur Szlam

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Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang

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Risk Bounds for Unsupervised Cross-Domain Mapping with IPMs Tomer Galanti, Sagie Benaim, Lior Wolf

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Risk-Averse Learning by Temporal Difference Methods with Markov Risk Measures Umit Köse, Andrzej Ruszczyński

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ROOTS: Object-Centric Representation and Rendering of 3D Scenes Chang Chen, Fei Deng, Sungjin Ahn

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Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach Alberto Maria Metelli, Matteo Pirotta, Daniele Calandriello, Marcello Restelli

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Shape-Enforcing Operators for Generic Point and Interval Estimators of Functions Xi Chen, Victor Chernozhukov, Ivan Fernandez-Val, Scott Kostyshak, Ye Luo

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Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-Examples Jagdeep Singh Bhatia

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Simultaneous Change Point Inference and Structure Recovery for High Dimensional Gaussian Graphical Models Bin Liu, Xinsheng Zhang, Yufeng Liu

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Single and Multiple Change-Point Detection with Differential Privacy Wanrong Zhang, Sara Krehbiel, Rui Tuo, Yajun Mei, Rachel Cummings

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Soft Tensor Regression Georgia Papadogeorgou, Zhengwu Zhang, David B. Dunson

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Some Theoretical Insights into Wasserstein GANs Gérard Biau, Maxime Sangnier, Ugo Tanielian

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Sparse and Smooth Signal Estimation: Convexification of L0-Formulations Alper Atamturk, Andres Gomez, Shaoning Han

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Sparse Convex Optimization via Adaptively Regularized Hard Thresholding Kyriakos Axiotis, Maxim Sviridenko

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Sparse Popularity Adjusted Stochastic Block Model Majid Noroozi, Marianna Pensky, Ramchandra Rimal

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Sparse Tensor Additive Regression Botao Hao, Boxiang Wang, Pengyuan Wang, Jingfei Zhang, Jian Yang, Will Wei Sun

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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

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Statistical Guarantees for Local Graph Clustering Wooseok Ha, Kimon Fountoulakis, Michael W. Mahoney

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Statistical Guarantees for Local Spectral Clustering on Random Neighborhood Graphs Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani

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Statistical Query Lower Bounds for Tensor PCA Rishabh Dudeja, Daniel Hsu

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Statistically and Computationally Efficient Change Point Localization in Regression Settings Daren Wang, Zifeng Zhao, Kevin Z. Lin, Rebecca Willett

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Stochastic Online Optimization Using Kalman Recursion Joseph de Vilmarest, Olivier Wintenberger

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Stochastic Proximal AUC Maximization Yunwen Lei, Yiming Ying

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Stochastic Proximal Methods for Non-Smooth Non-Convex Constrained Sparse Optimization Michael R. Metel, Akiko Takeda

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Strong Consistency, Graph Laplacians, and the Stochastic Block Model Shaofeng Deng, Shuyang Ling, Thomas Strohmer

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Structure Learning of Undirected Graphical Models for Count Data Nguyen Thi Kim Hue, Monica Chiogna

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Subspace Clustering Through Sub-Clusters Weiwei Li, Jan Hannig, Sayan Mukherjee

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Testing Conditional Independence via Quantile Regression Based Partial Copulas Lasse Petersen, Niels Richard Hansen

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The Decoupled Extended Kalman Filter for Dynamic Exponential-Family Factorization Models Carlos A. Gomez-Uribe, Brian Karrer

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The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks Takuo Matsubara, Chris J. Oates, François-Xavier Briol

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Thompson Sampling Algorithms for Cascading Bandits Zixin Zhong, Wang Chi Chueng, Vincent Y. F. Tan

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Tighter Risk Certificates for Neural Networks María Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor, Csaba Szepesvári

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Towards a Unified Analysis of Random Fourier Features Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic

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Tractable Approximate Gaussian Inference for Bayesian Neural Networks James-A. Goulet, Luong Ha Nguyen, Saeid Amiri

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Transferability of Spectral Graph Convolutional Neural Networks Ron Levie, Wei Huang, Lorenzo Bucci, Michael Bronstein, Gitta Kutyniok

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Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits Julian Zimmert, Yevgeny Seldin

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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

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Understanding Recurrent Neural Networks Using Nonequilibrium Response Theory Soon Hoe Lim

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Unfolding-Model-Based Visualization: Theory, Method and Applications Yunxiao Chen, Zhiliang Ying, Haoran Zhang

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Universal Consistency and Rates of Convergence of Multiclass Prototype Algorithms in Metric Spaces László Györfi, Roi Weiss

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Unlinked Monotone Regression Fadoua Balabdaoui, Charles R. Doss, Cécile Durot

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V-Statistics and Variance Estimation Zhengze Zhou, Lucas Mentch, Giles Hooker

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Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference Jiyuan Tu, Weidong Liu, Xiaojun Mao, Xi Chen

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VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning Luisa Zintgraf, Sebastian Schulze, Cong Lu, Leo Feng, Maximilian Igl, Kyriacos Shiarlis, Yarin Gal, Katja Hofmann, Shimon Whiteson

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Wasserstein Barycenters Can Be Computed in Polynomial Time in Fixed Dimension Jason M Altschuler, Enric Boix-Adsera

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Wasserstein Distance Estimates for the Distributions of Numerical Approximations to Ergodic Stochastic Differential Equations Jesus Maria Sanz-Serna, Konstantinos C. Zygalakis

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What Causes the Test Error? Going Beyond Bias-Variance via ANOVA Licong Lin, Edgar Dobriban

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When Does Gradient Descent with Logistic Loss Find Interpolating Two-Layer Networks? Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett

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When Random Initializations Help: A Study of Variational Inference for Community Detection Purnamrita Sarkar, Y. X. Rachel Wang, Soumendu S. Mukherjee

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