JMLR 2022

353 papers

Truncated Emphatic Temporal Difference Methods for Prediction and Control

Shangtong Zhang, Shimon Whiteson

Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning

Sébastien Forestier, Rémy Portelas, Yoan Mollard, Pierre-yves Oudeyer

Universal Approximation of Functions on Sets

Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke, Michael A. Osborne, Ingmar Posner

EV-GAN: Simulation of extreme events with ReLU neural networks

Michaël Allouche, Stéphane Girard, Emmanuel Gobet

Implicit Differentiation for Fast Hyperparameter Selection in Non-Smooth Convex Learning

Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter

Online Nonnegative CP-dictionary Learning for Markovian Data

Hanbaek Lyu, Christopher Strohmeier, Deanna Needell

On the Robustness to Misspecification of α-posteriors and Their Variational Approximations

Marco Avella Medina, José Luis Montiel Olea, Cynthia Rush, Amilcar Velez

Adversarial Robustness Guarantees for Gaussian Processes

Andrea Patane, Arno Blaas, Luca Laurenti, Luca Cardelli, Stephen Roberts

A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning

Andrew Patterson, Adam White, Martha White

A Momentumized, Adaptive, Dual Averaged Gradient Method

Aaron Defazio, Samy Jelassi

Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning

Alex A. Gorodetsky, Cosmin Safta, John D. Jakeman

A Perturbation-Based Kernel Approximation Framework

Roy Mitz, Yoel Shkolnisky

The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks

Konstantinos Pantazis, Avanti Athreya, Jesús Arroyo, William N Frost, Evan S Hill

Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems

Yahya Sattar, Samet Oymak

No Weighted-Regret Learning in Adversarial Bandits with Delays

Ilai Bistritz, Zhengyuan Zhou, Xi Chen, Nicholas Bambos, Jose Blanchet

Exact simulation of diffusion first exit times: algorithm acceleration

Samuel Herrmann, Cristina Zucca

On the Efficiency of Entropic Regularized Algorithms for Optimal Transport

Tianyi Lin, nhật Hồ, Michael I. Jordan

Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization

Quanming Yao, Yaqing Wang, Bo Han, James T. Kwok

Recovery and Generalization in Over-Realized Dictionary Learning

Jeremias Sulam, Chong You, Zhihui Zhu

Transfer Learning in Information Criteria-based Feature Selection

Shaohan Chen, Nikolaos V. Sahinidis, Chuanhou Gao

Manifold Coordinates with Physical Meaning

Samson J. Koelle, Hanyu Zhang, Marina Meila, Yu-Chia Chen

An Optimization-centric View on Bayes' Rule: Reviewing and Generalizing Variational Inference

Jeremias Knoblauch, Jack Jewson, Theodoros Damoulas

Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence

Julie Nutini, Issam Laradji, Mark Schmidt

Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks

Alexander Shevchenko, Vyacheslav Kungurtsev, Marco Mondelli

On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)

Washim Uddin Mondal, Mridul Agarwal, Vaneet Aggarwal, Satish V. Ukkusuri

Power Iteration for Tensor PCA

Jiaoyang Huang, Daniel Z. Huang, Qing Yang, Guang Cheng

Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations

Haoyuan Chen, Liang Ding, Rui Tuo

Neural Estimation of Statistical Divergences

Sreejith Sreekumar, Ziv Goldfeld

Foolish Crowds Support Benign Overfitting

Niladri S. Chatterji, Philip M. Long

Darts: User-Friendly Modern Machine Learning for Time Series

Julien Herzen, Francesco Lässig, Samuele Giuliano Piazzetta, Thomas Neuer, Léo Tafti

Provable Tensor-Train Format Tensor Completion by Riemannian Optimization

Jian-Feng CAI, Jingyang Li, Dong Xia

Depth separation beyond radial functions

Luca Venturi, Samy Jelassi, Tristan Ozuch, Joan Bruna

Online Mirror Descent and Dual Averaging: Keeping Pace in the Dynamic Case

Huang Fang, Nicholas J. A. Harvey, Victor S. Portella, Michael P. Friedlander

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

William Fedus, Barret Zoph, Noam Shazeer

A spectral-based analysis of the separation between two-layer neural networks and linear methods

Lei Wu, Jihao Long

Under-bagging Nearest Neighbors for Imbalanced Classification

Hanyuan Hang, Yuchao Cai, Hanfang Yang, Zhouchen Lin

OVERT: An Algorithm for Safety Verification of Neural Network Control Policies for Nonlinear Systems

Chelsea Sidrane, Amir Maleki, Ahmed Irfan, Mykel J. Kochenderfer

An Error Analysis of Generative Adversarial Networks for Learning Distributions

Jian Huang, Yuling Jiao, Zhen Li, Shiao Liu, Yang Wang

Cauchy–Schwarz Regularized Autoencoder

Linh Tran, Maja Pantic, Marc Peter Deisenroth

ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction

Kwan Ho Ryan Chan, Yaodong Yu, Chong You, Haozhi Qi, John Wright

The Two-Sided Game of Googol

Jose Correa, Andres Cristi, Boris Epstein, José Soto

Sum of Ranked Range Loss for Supervised Learning

Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu

Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces

Masaaki Imaizumi, Kenji Fukumizu

EiGLasso for Scalable Sparse Kronecker-Sum Inverse Covariance Estimation

Jun Ho Yoon, Seyoung Kim

Conditions and Assumptions for Constraint-based Causal Structure Learning

Kayvan Sadeghi, Terry Soo

Bayesian subset selection and variable importance for interpretable prediction and classification

Daniel R. Kowal

IALE: Imitating Active Learner Ensembles

Christoffer Löffler, Christopher Mutschler

Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold

Bokun Wang, Shiqian Ma, Lingzhou Xue

Globally Injective ReLU Networks

Michael Puthawala, Konik Kothari, Matti Lassas, Ivan Dokmanić, Maarten de Hoop

Efficient Least Squares for Estimating Total Effects under Linearity and Causal Sufficiency

F. Richard Guo, Emilija Perkovic

The EM Algorithm is Adaptively-Optimal for Unbalanced Symmetric Gaussian Mixtures

Nir Weinberger, Guy Bresler

Sufficient reductions in regression with mixed predictors

Efstathia Bura, Liliana Forzani, Rodrigo Garcia Arancibia, Pamela Llop, Diego Tomassi

Towards An Efficient Approach for the Nonconvex lp Ball Projection: Algorithm and Analysis

Xiangyu Yang, Jiashan Wang, Hao Wang

Total Stability of SVMs and Localized SVMs

Hannes Köhler, Andreas Christmann

Distributed Learning of Finite Gaussian Mixtures

Qiong Zhang, Jiahua Chen

PECOS: Prediction for Enormous and Correlated Output Spaces

Hsiang-Fu Yu, Kai Zhong, Jiong Zhang, Wei-Cheng Chang, Inderjit S. Dhillon

Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective

Daniel Sanz-Alonso, Ruiyi Yang

Rethinking Nonlinear Instrumental Variable Models through Prediction Validity

Chunxiao Li, Cynthia Rudin, Tyler H. McCormick

Attraction-Repulsion Spectrum in Neighbor Embeddings

Jan Niklas Böhm, Philipp Berens, Dmitry Kobak

Multiple Testing in Nonparametric Hidden Markov Models: An Empirical Bayes Approach

Kweku Abraham, Ismaël Castillo, Elisabeth Gassiat

Regularized K-means Through Hard-Thresholding

Jakob Raymaekers, Ruben H. Zamar

Gauss-Legendre Features for Gaussian Process Regression

Paz Fink Shustin, Haim Avron

When Hardness of Approximation Meets Hardness of Learning

Eran Malach, Shai Shalev-shwartz

Accelerating Adaptive Cubic Regularization of Newton's Method via Random Sampling

Xi Chen, Bo Jiang, Tianyi Lin, Shuzhong Zhang

Machine Learning on Graphs: A Model and Comprehensive Taxonomy

Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Re, Kevin Murphy

Generalized Ambiguity Decomposition for Ranking Ensemble Learning

Hongzhi Liu, Yingpeng Du, Zhonghai Wu

CD-split and HPD-split: Efficient Conformal Regions in High Dimensions

Rafael Izbicki, Gilson Shimizu, Rafael B. Stern

Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing

Chao Shen, Yu-Ting Lin, Hau-Tieng Wu

A Distribution Free Conditional Independence Test with Applications to Causal Discovery

Zhanrui Cai, Runze Li, Yaowu Zhang

Distributed Bayesian Varying Coefficient Modeling Using a Gaussian Process Prior

Rajarshi Guhaniyogi, Cheng Li, Terrance D. Savitsky, Sanvesh Srivastava

Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping

Yichi Zhang, Molei Liu, Matey Neykov, Tianxi Cai

FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting

Boxin Zhao, Y. Samuel Wang, Mladen Kolar

Dependent randomized rounding for clustering and partition systems with knapsack constraints

David G. Harris, Thomas Pensyl, Aravind Srinivasan, Khoa Trinh

Posterior Asymptotics for Boosted Hierarchical Dirichlet Process Mixtures

Marta Catalano, Pierpaolo De Blasi, Antonio Lijoi, Igor Pruenster

Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors

Yuling Yao, Aki Vehtari, Andrew Gelman

Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism

Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos

Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits

Lilian Besson, Emilie Kaufmann, Odalric-ambrym Maillard, Julien Seznec

Joint Inference of Multiple Graphs from Matrix Polynomials

Madeline Navarro, Yuhao Wang, Antonio G. Marques, Caroline Uhler, Santiago Segarra

Mutual Information Constraints for Monte-Carlo Objectives to Prevent Posterior Collapse Especially in Language Modelling

Gábor Melis, András György, Phil Blunsom

All You Need is a Good Functional Prior for Bayesian Deep Learning

Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Maurizio Filippone

A Kernel Two-Sample Test for Functional Data

George Wynne, Andrew B. Duncan

Batch Normalization Preconditioning for Neural Network Training

Susanna Lange, Kyle Helfrich, Qiang Ye

Multiple-Splitting Projection Test for High-Dimensional Mean Vectors

Wanjun Liu, Xiufan Yu, Runze Li

Generalized Sparse Additive Models

Asad Haris, Noah Simon, Ali Shojaie

Asymptotic Network Independence and Step-Size for a Distributed Subgradient Method

Alex Olshevsky

Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters

Wei Zhu, Qiang Qiu, Robert Calderbank, Guillermo Sapiro, Xiuyuan Cheng

Are All Layers Created Equal?

Chiyuan Zhang, Samy Bengio, Yoram Singer

New Insights for the Multivariate Square-Root Lasso

Aaron J. Molstad

On the Complexity of Approximating Multimarginal Optimal Transport

Tianyi Lin, nhật Hồ, Marco Cuturi, Michael I. Jordan

Stochastic Zeroth-Order Optimization under Nonstationarity and Nonconvexity

Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra

Additive Nonlinear Quantile Regression in Ultra-high Dimension

Ben Sherwood, Adam Maidman

The AIM and EM Algorithms for Learning from Coarse Data

Manfred Jaeger

Sparse Additive Gaussian Process Regression

Hengrui Luo, Giovanni Nattino, Matthew T. Pratola

A Unifying Framework for Variance-Reduced Algorithms for Findings Zeroes of Monotone operators

Xun Zhang, William B. Haskell, Zhisheng Ye

Causal Classification: Treatment Effect Estimation vs. Outcome Prediction

Carlos Fernández-Loría, Foster Provost

A Statistical Approach for Optimal Topic Model Identification

Craig M. Lewis, Francesco Grossetti

Inherent Tradeoffs in Learning Fair Representations

Han Zhao, Geoffrey J. Gordon

solo-learn: A Library of Self-supervised Methods for Visual Representation Learning

Victor Guilherme Turrisi da Costa, Enrico Fini, Moin Nabi, Nicu Sebe, Elisa Ricci

Bayesian Pseudo Posterior Mechanism under Asymptotic Differential Privacy

Terrance D. Savitsky, Matthew R.Williams, Jingchen Hu

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng