JMLR 2022

353 papers

On quantitative Laplace-type convergence results for some exponential probability measures, with two applications

Valentin De Bortoli, Agnes Desolneux

Averaging Spatio-temporal Signals using Optimal Transport and Soft Alignment

Hicham Janati, Marco Cuturi, Alexandre Gramfort

Metrics of Calibration for Probabilistic Predictions

Imanol Arrieta-Ibarra, Paman Gujral, Jonathan Tannen, Mark Tygert, Cherie Xu

Approximate Bayesian Computation via Classification

Yuexi Wang, Tetsuya Kaji, Veronika Ročková

OMLT: Optimization & Machine Learning Toolkit

Francesco Ceccon, Jordan Jalving, Joshua Haddad, Alexander Thebelt, Calvin Tsay

Scalable Gaussian-process regression and variable selection using Vecchia approximations

Jian Cao, Joseph Guinness, Marc G. Genton, Matthias Katzfuss

Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks

El Mehdi Achour, François Malgouyres, Franck Mamalet

Minimax optimal approaches to the label shift problem in non-parametric settings

Subha Maity, Yuekai Sun, Moulinath Banerjee

Constraint Reasoning Embedded Structured Prediction

Nan Jiang, Maosen Zhang, Willem-Jan van Hoeve, Yexiang Xue

Vector-Valued Least-Squares Regression under Output Regularity Assumptions

Luc Brogat-Motte, Alessandro Rudi, Céline Brouard, Juho Rousu, Florence d'Alché-Buc

Global Optimality and Finite Sample Analysis of Softmax Off-Policy Actor Critic under State Distribution Mismatch

Shangtong Zhang, Remi Tachet des Combes, Romain Laroche

SGD with Coordinate Sampling: Theory and Practice

Rémi Leluc, François Portier

Information-theoretic Classification Accuracy: A Criterion that Guides Data-driven Combination of Ambiguous Outcome Labels in Multi-class Classification

Chihao Zhang, Yiling Elaine Chen, Shihua Zhang, Jingyi Jessica Li

Fundamental Limits and Tradeoffs in Invariant Representation Learning

Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon

Early Stopping for Iterative Regularization with General Loss Functions

Ting Hu, Yunwen Lei

Interval-censored Hawkes processes

Marian-Andrei Rizoiu, Alexander Soen, Shidi Li, Pio Calderon, Leanne J. Dong

Statistical Optimality and Computational Efficiency of Nystrom Kernel PCA

Nicholas Sterge, Bharath K. Sriperumbudur

Faster Randomized Interior Point Methods for Tall/Wide Linear Programs

Agniva Chowdhury, Gregory Dexter, Palma London, Haim Avron, Petros Drineas

Causal Aggregation: Estimation and Inference of Causal Effects by Constraint-Based Data Fusion

Jaime Roquero Gimenez, Dominik Rothenhäusler

Fully General Online Imitation Learning

Michael K. Cohen, Marcus Hutter, Neel Nanda

Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression

Domagoj Ćevid, Loris Michel, Jeffrey Näf, Peter Bühlmann, Nicolai Meinshausen

Maximum sampled conditional likelihood for informative subsampling

HaiYing Wang, Jae Kwang Kim

The Geometry of Uniqueness, Sparsity and Clustering in Penalized Estimation

Ulrike Schneider, Patrick Tardivel

ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Network

Xing Fan, Marianna Pensky, Feng Yu, Teng Zhang

Joint Continuous and Discrete Model Selection via Submodularity

Jonathan Bunton, Paulo Tabuada

Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima

Brian Swenson, Ryan Murray, H. Vincent Poor, Soummya Kar

Kernel Autocovariance Operators of Stationary Processes: Estimation and Convergence

Mattes Mollenhauer, Stefan Klus, Christof Schütte, Péter Koltai

Project and Forget: Solving Large-Scale Metric Constrained Problems

Rishi Sonthalia, Anna C. Gilbert

On Mixup Regularization

Luigi Carratino, Moustapha Cisse, Rodolphe Jenatton, Jean-philippe Vert

Improving Bayesian Network Structure Learning in the Presence of Measurement Error

Yang Liu, Anthony C. Constantinou, Zhigao Guo

Convergence Rates for Gaussian Mixtures of Experts

nhật Hồ, Chiao-Yu Yang, Michael I. Jordan

Community detection in sparse latent space models

Fengnan Gao, Zongming Ma, Hongsong Yuan

On Low-rank Trace Regression under General Sampling Distribution

Nima Hamidi, Mohsen Bayati

Network Regression with Graph Laplacians

Yidong Zhou, Hans-Georg Müller

Self-Healing Robust Neural Networks via Closed-Loop Control

Zhuotong Chen, Qianxiao Li, Zheng Zhang

Hamilton-Jacobi equations on graphs with applications to semi-supervised learning and data depth

Jeff Calder, Mahmood Ettehad

Nonparametric Neighborhood Selection in Graphical Models

Hao Dong, Yuedong Wang

WarpDrive: Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

Tian Lan, Sunil Srinivasa, Huan Wang, Stephan Zheng

d3rlpy: An Offline Deep Reinforcement Learning Library

Takuma Seno, Michita Imai

Oracle Complexity in Nonsmooth Nonconvex Optimization

Guy Kornowski, Ohad Shamir

Intrinsic Dimension Estimation Using Wasserstein Distance

Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin

Nystrom Regularization for Time Series Forecasting

Zirui Sun, Mingwei Dai, Yao Wang, Shao-Bo Lin

Toward Understanding Convolutional Neural Networks from Volterra Convolution Perspective

Tenghui Li, Guoxu Zhou, Yuning Qiu, Qibin Zhao

Detecting Latent Communities in Network Formation Models

Shujie Ma, Liangjun Su, Yichong Zhang

The Separation Capacity of Random Neural Networks

Sjoerd Dirksen, Martin Genzel, Laurent Jacques, Alexander Stollenwerk

On Regularized Square-root Regression Problems: Distributionally Robust Interpretation and Fast Computations

Hong T.M. Chu, Kim-Chuan Toh, Yangjing Zhang

Learning from Noisy Pairwise Similarity and Unlabeled Data

Songhua Wu, Tongliang Liu, Bo Han, Jun Yu, Gang Niu

Pathfinder: Parallel quasi-Newton variational inference

Lu Zhang, Bob Carpenter, Andrew Gelman, Aki Vehtari

Tree-Values: Selective Inference for Regression Trees

Anna C. Neufeld, Lucy L. Gao, Daniela M. Witten

Variational Inference in high-dimensional linear regression

Sumit Mukherjee, Subhabrata Sen

Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks

Alice Gatti, Zhixiong Hu, Tess Smidt, Esmond G. Ng, Pieter Ghysels

On Instrumental Variable Regression for Deep Offline Policy Evaluation

Yutian Chen, Liyuan Xu, Caglar Gulcehre, Tom Le Paine, Arthur Gretton

Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data

T. Tony Cai, Rong Ma

More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programming

Vo Nguyen Le Duy, Ichiro Takeuchi

Interpretable Classification of Categorical Time Series Using the Spectral Envelope and Optimal Scalings

Zeda Li, Scott A. Bruce, Tian Cai

JsonGrinder.jl: automated differentiable neural architecture for embedding arbitrary JSON data

Šimon Mandlík, Matěj Račinský, Viliam Lisy, Tomas Pevny

Handling Hard Affine SDP Shape Constraints in RKHSs

Pierre-Cyril Aubin-Frankowski, Zoltan Szabo

Stable Classification

Dimitris Bertsimas, Jack Dunn, Ivan Paskov

Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables

Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser

Linearization and Identification of Multiple-Attractor Dynamical Systems through Laplacian Eigenmaps

Bernardo Fichera, Aude Billard

Expected Regret and Pseudo-Regret are Equivalent When the Optimal Arm is Unique

Daron Anderson, Douglas J. Leith

Two-mode Networks: Inference with as Many Parameters as Actors and Differential Privacy

Qiuping Wang, Ting Yan, Binyan Jiang, Chenlei Leng

Generalized Matrix Factorization: efficient algorithms for fitting generalized linear latent variable models to large data arrays

Lukasz Kidzinski, Francis K.C. Hui, David I. Warton, Trevor J. Hastie

Regularized and Smooth Double Core Tensor Factorization for Heterogeneous Data

Davoud Ataee Tarzanagh, George Michailidis

Estimating Causal Effects under Network Interference with Bayesian Generalized Propensity Scores

Laura Forastiere, Fabrizia Mealli, Albert Wu, Edoardo M. Airoldi

ReservoirComputing.jl: An Efficient and Modular Library for Reservoir Computing Models

Francesco Martinuzzi, Chris Rackauckas, Anas Abdelrehim, Miguel D. Mahecha, Karin Mora

Information-Theoretic Characterization of the Generalization Error for Iterative Semi-Supervised Learning

Haiyun He, Hanshu Yan, Vincent Y. F. Tan

Integral Autoencoder Network for Discretization-Invariant Learning

Yong Zheng Ong, Zuowei Shen, Haizhao Yang

Deepchecks: A Library for Testing and Validating Machine Learning Models and Data

Shir Chorev, Philip Tannor, Dan Ben Israel, Noam Bressler, Itay Gabbay

Exact Partitioning of High-order Models with a Novel Convex Tensor Cone Relaxation

Chuyang Ke, Jean Honorio

De-Sequentialized Monte Carlo: a parallel-in-time particle smoother

Adrien Corenflos, Nicolas Chopin, Simo Särkkä

On the Convergence Rates of Policy Gradient Methods

Lin Xiao

Nonparametric adaptive control and prediction: theory and randomized algorithms

Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine

Generalized Resubstitution for Classification Error Estimation

Parisa Ghane, Ulisses Braga-Neto

Convergence Guarantees for the Good-Turing Estimator

Amichai Painsky

Jump Gaussian Process Model for Estimating Piecewise Continuous Regression Functions

Chiwoo Park

Nonstochastic Bandits with Composite Anonymous Feedback

Nicolò Cesa-bianchi, Tommaso Cesari, Roberto Colomboni, Claudio Gentile, Yishay Mansour

Deep Network Approximation: Achieving Arbitrary Accuracy with Fixed Number of Neurons

Shijun Zhang, Zuowei Shen, Haizhao Yang

Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithms

Yanwei Jia, Xun Yu Zhou

CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms

Shengyi Huang, Rousslan Fernand Julien Dossa, Chang Ye, Jeff Braga, Dipam Chakraborty

The Weighted Generalised Covariance Measure

Cyrill Scheidegger, Julia Hörrmann, Peter Bühlmann

Communication-Constrained Distributed Quantile Regression with Optimal Statistical Guarantees

Kean Ming Tan, Heather Battey, Wen-Xin Zhou

Fast Stagewise Sparse Factor Regression

Kun Chen, Ruipeng Dong, Wanwan Xu, Zemin Zheng

Minimax Mixing Time of the Metropolis-Adjusted Langevin Algorithm for Log-Concave Sampling

Keru Wu, Scott Schmidler, Yuansi Chen

Learning linear non-Gaussian directed acyclic graph with diverging number of nodes

Ruixuan Zhao, XIN HE, Junhui Wang

A Computationally Efficient Framework for Vector Representation of Persistence Diagrams

Kit C Chan, Umar Islambekov, Alexey Luchinsky, Rebecca Sanders

Tianshou: A Highly Modularized Deep Reinforcement Learning Library

Jiayi Weng, Huayu Chen, Dong Yan, Kaichao You, Alexis Duburcq

Functional Linear Regression with Mixed Predictors

Daren Wang, Zifeng Zhao, Yi Yu, Rebecca Willett

Stochastic subgradient for composite convex optimization with functional constraints

Ion Necoara, Nitesh Kumar Singh

A Random Matrix Perspective on Random Tensors

José Henrique de M. Goulart, Romain Couillet, Pierre Comon

The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks

Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett

Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score

Muxuan Liang, Young-Geun Choi, Yang Ning, Maureen A Smith, Ying-Qi Zhao

Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning

Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter

A proof of convergence for the gradient descent optimization method with random initializations in the training of neural networks with ReLU activation for piecewise linear target functions

Arnulf Jentzen, Adrian Riekert

Learning Temporal Evolution of Spatial Dependence with Generalized Spatiotemporal Gaussian Process Models

Shiwei Lan

Tree-Based Models for Correlated Data

Assaf Rabinowicz, Saharon Rosset

Sparse Continuous Distributions and Fenchel-Young Losses

André F. T. Martins, Marcos Treviso, António Farinhas, Pedro M. Q. Aguiar, Mario A. T. Figueiredo

On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems

Michael Muehlebach, Michael I. Jordan

Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States

Shi Dong, Benjamin Van Roy, Zhengyuan Zhou

Adaptive Greedy Algorithm for Moderately Large Dimensions in Kernel Conditional Density Estimation

Minh-Lien Jeanne Nguyen, Claire Lacour, Vincent Rivoirard