JMLR 2023

401 papers

Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds

Didong Li, Wenpin Tang, Sudipto Banerjee

FedLab: A Flexible Federated Learning Framework

Dun Zeng, SIQI LIANG, Xiangjing Hu, Hui Wang, Zenglin Xu

Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity

Artem Vysogorets, Julia Kempe

An Analysis of Robustness of Non-Lipschitz Networks

Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang

Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation

Xu Han, Xiaohui Chen, Francisco J. R. Ruiz, Li-Ping Liu

Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization

Jianhao Ma, Salar Fattahi

Statistical Inference for Noisy Incomplete Binary Matrix

Yunxiao Chen, Chengcheng Li, Jing Ouyang, Gongjun Xu

Faith-Shap: The Faithful Shapley Interaction Index

Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar

Decentralized Learning: Theoretical Optimality and Practical Improvements

Yucheng Lu, Christopher De Sa

Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption

Lihu Xu, Fang Yao, Qiuran Yao, Huiming Zhang

Recursive Quantile Estimation: Non-Asymptotic Confidence Bounds

Likai Chen, Georg Keilbar, Wei Biao Wu

Outlier-Robust Subsampling Techniques for Persistent Homology

Bernadette J. Stolz

Neural Operator: Learning Maps Between Function Spaces With Applications to PDEs

Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya

Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data

Yuqi Gu, Elena E. Erosheva, Gongjun Xu, David B. Dunson

Gaussian Processes with Errors in Variables: Theory and Computation

Shuang Zhou, Debdeep Pati, Tianying Wang, Yun Yang, Raymond J. Carroll

Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces

George Stepaniants

Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence

Henry Lam, Haofeng Zhang

Online Optimization over Riemannian Manifolds

Xi Wang, Zhipeng Tu, Yiguang Hong, Yingyi Wu, Guodong Shi

Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees

William J. Wilkinson, Simo Särkkä, Arno Solin

Iterated Block Particle Filter for High-dimensional Parameter Learning: Beating the Curse of Dimensionality

Ning Ning, Edward L. Ionides

Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics

Gaetano Romano, Idris A. Eckley, Paul Fearnhead, Guillem Rigaill

Temporal Abstraction in Reinforcement Learning with the Successor Representation

Marlos C. Machado, Andre Barreto, Doina Precup, Michael Bowling

Approximate Post-Selective Inference for Regression with the Group LASSO

Snigdha Panigrahi, Peter W MacDonald, Daniel Kessler

Towards Learning to Imitate from a Single Video Demonstration

Glen Berseth, Florian Golemo, Christopher Pal

A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models

Minwoo Chae, Dongha Kim, Yongdai Kim, Lizhen Lin

A Randomized Subspace-based Approach for Dimensionality Reduction and Important Variable Selection

Di Bo, Hoon Hwangbo, Vinit Sharma, Corey Arndt, Stephanie TerMaath

Intrinsic Persistent Homology via Density-based Metric Learning

Ximena Fernández, Eugenio Borghini, Gabriel Mindlin, Pablo Groisman

Privacy-Aware Rejection Sampling

Jordan Awan, Vinayak Rao

Inference for a Large Directed Acyclic Graph with Unspecified Interventions

Chunlin Li, Xiaotong Shen, Wei Pan

How Do You Want Your Greedy: Simultaneous or Repeated?

Moran Feldman, Christopher Harshaw, Amin Karbasi

Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition

Simon Bartels, Wouter Boomsma, Jes Frellsen, Damien Garreau

Optimizing ROC Curves with a Sort-Based Surrogate Loss for Binary Classification and Changepoint Detection

Jonathan Hillman, Toby Dylan Hocking

When Locally Linear Embedding Hits Boundary

Hau-Tieng Wu, Nan Wu

Distributed Nonparametric Regression Imputation for Missing Response Problems with Large-scale Data

Ruoyu Wang, Miaomiao Su, Qihua Wang

Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching

Eliezer de Souza da Silva, Tomasz Kuśmierczyk, Marcelo Hartmann, Arto Klami

Posterior Contraction for Deep Gaussian Process Priors

Gianluca Finocchio, Johannes Schmidt-Hieber

Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule

Nikhil Iyer, V. Thejas, Nipun Kwatra, Ramachandran Ramjee, Muthian Sivathanu

Fundamental limits and algorithms for sparse linear regression with sublinear sparsity

Lan V. Truong

On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results

Marcelo Arenas, Pablo Barceló, Leopoldo Bertossi, Mikaël Monet

Monotonic Alpha-divergence Minimisation for Variational Inference

Kamélia Daudel, randal douc, François Roueff

Density estimation on low-dimensional manifolds: an inflation-deflation approach

Christian Horvat, Jean-pascal Pfister

Provably Sample-Efficient Model-Free Algorithm for MDPs with Peak Constraints

Qinbo Bai, Vaneet Aggarwal, Ather Gattami

Topological Convolutional Layers for Deep Learning

Ephy R. Love, Benjamin Filippenko, Vasileios Maroulas, Gunnar Carlsson

Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-smooth, Non-convex Phase Retrieval

Yan Shuo Tan, Roman Vershynin

Tree-AMP: Compositional Inference with Tree Approximate Message Passing

Antoine Baker, Florent Krzakala, Benjamin Aubin, Lenka Zdeborová

On the geometry of Stein variational gradient descent

Andrew Duncan, Nikolas Nüsken, Lukasz Szpruch

Kernel-based estimation for partially functional linear model: Minimax rates and randomized sketches

shaogao lv, XIN HE, Junhui Wang

Contextual Stochastic Block Model: Sharp Thresholds and Contiguity

Chen Lu, Subhabrata Sen

VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback

Kirthevasan Kandasamy, Joseph E Gonzalez, Michael I Jordan, Ion Stoica

Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems

Kunal Pattanayak, Vikram Krishnamurthy

Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks

Lingjun Li, Jun Li

Convergence Rates of a Class of Multivariate Density Estimation Methods Based on Adaptive Partitioning

Linxi Liu, Dangna Li, Wing Hung Wong

Reinforcement Learning for Joint Optimization of Multiple Rewards

Mridul Agarwal, Vaneet Aggarwal

On the Convergence of Stochastic Gradient Descent with Bandwidth-based Step Size

Xiaoyu Wang, Ya-xiang Yuan

A Group-Theoretic Approach to Computational Abstraction: Symmetry-Driven Hierarchical Clustering

Haizi Yu, Igor Mineyev, Lav R. Varshney

The d-Separation Criterion in Categorical Probability

Tobias Fritz, Andreas Klingler

The multimarginal optimal transport formulation of adversarial multiclass classification

Nicolás García Trillos, Matt Jacobs, Jakwang Kim

Robust Load Balancing with Machine Learned Advice

Sara Ahmadian, Hossein Esfandiari, Vahab Mirrokni, Binghui Peng

Benchmarking Graph Neural Networks

Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio

A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness

Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen

Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data

Shaowu Pan, Steven L. Brunton, J. Nathan Kutz

On Batch Teaching Without Collusion

Shaun Fallat, David Kirkpatrick, Hans U. Simon, Abolghasem Soltani, Sandra Zilles

Sensing Theorems for Unsupervised Learning in Linear Inverse Problems

Julián Tachella, Dongdong Chen, Mike Davies

First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems

Michael I. Jordan, Tianyi Lin, Manolis Zampetakis

Ridges, Neural Networks, and the Radon Transform

Michael Unser

Label Distribution Changing Learning with Sample Space Expanding

Chao Xu, Hong Tao, Jing Zhang, Dewen Hu, Chenping Hou

Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers?

Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan

Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond

Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus

Gap Minimization for Knowledge Sharing and Transfer

Boyu Wang, Jorge A. Mendez, Changjian Shui, Fan Zhou, Di Wu

Sparse PCA: a Geometric Approach

Dimitris Bertsimas, Driss Lahlou Kitane

Labels, Information, and Computation: Efficient Learning Using Sufficient Labels

Shiyu Duan, Spencer Chang, José C. Príncipe

Attacks against Federated Learning Defense Systems and their Mitigation

Cody Lewis, Vijay Varadharajan, Nasimul Noman

HiClass: a Python Library for Local Hierarchical Classification Compatible with Scikit-learn

Fábio M. Miranda, Niklas Köhnecke, Bernhard Y. Renard

Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping

Xuran Meng, Jeff Yao

The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time

Raj Agrawal, Tamara Broderick

Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels

Hao Wang, RUI GAO, Flavio P. Calmon

Discrete Variational Calculus for Accelerated Optimization

Cédric M. Campos, Alejandro Mahillo, David Martín de Diego

Calibrated Multiple-Output Quantile Regression with Representation Learning

Shai Feldman, Stephen Bates, Yaniv Romano

Bayesian Data Selection

Eli N. Weinstein, Jeffrey W. Miller

Lower Bounds and Accelerated Algorithms for Bilevel Optimization

Kaiyi Ji, Yingbin Liang

Graph-Aided Online Multi-Kernel Learning

Pouya M. Ghari, Yanning Shen

Interpolating Classifiers Make Few Mistakes

Tengyuan Liang, Benjamin Recht

Regularized Joint Mixture Models

Konstantinos Perrakis, Thomas Lartigue, Frank Dondelinger, Sach Mukherjee

An Inertial Block Majorization Minimization Framework for Nonsmooth Nonconvex Optimization

Le Thi Khanh Hien, Duy Nhat Phan, Nicolas Gillis

Learning Mean-Field Games with Discounted and Average Costs

Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi

Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation

Cynthia Rudin, Yaron Shaposhnik

Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions

Jon Vadillo, Roberto Santana, Jose A. Lozano

Python package for causal discovery based on LiNGAM

Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu

Adaptation to the Range in K-Armed Bandits

Hedi Hadiji, Gilles Stoltz

Learning-augmented count-min sketches via Bayesian nonparametrics

Emanuele Dolera, Stefano Favaro, Stefano Peluchetti

Optimal Strategies for Reject Option Classifiers

Vojtěch Franc, Daniel Průša, Václav Voráček

A Line-Search Descent Algorithm for Strict Saddle Functions with Complexity Guarantees

Michael J. O'Neill, Stephen J. Wright

Sampling random graph homomorphisms and applications to network data analysis

Hanbaek Lyu, Facundo Mémoli, David Sivakoff

A Relaxed Inertial Forward-Backward-Forward Algorithm for Solving Monotone Inclusions with Application to GANs

Radu I. Bot, Michael Sedlmayer, Phan Tu Vuong

On Distance and Kernel Measures of Conditional Dependence

Tianhong Sheng, Bharath K. Sriperumbudur

AutoKeras: An AutoML Library for Deep Learning

Haifeng Jin, Francois Chollet, Qingquan Song, Xia Hu

Cluster-Specific Predictions with Multi-Task Gaussian Processes

Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey

Efficient Structure-preserving Support Tensor Train Machine

Kirandeep Kour, Sergey Dolgov, Martin Stoll, Peter Benner

Bayesian Spiked Laplacian Graphs

Leo L Duan, George Michailidis, Mingzhou Ding

The Brier Score under Administrative Censoring: Problems and a Solution

Håvard Kvamme, Ørnulf Borgan