JMLR 2021

290 papers

Collusion Detection and Ground Truth Inference in Crowdsourcing for Labeling Tasks

Changyue Song, Kaibo Liu, Xi Zhang

One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them

Saber Salehkaleybar, Arsalan Sharifnassab, S. Jamaloddin Golestani

Differentially Private Regression and Classification with Sparse Gaussian Processes

Michael Thomas Smith, Mauricio A. Álvarez, Neil D. Lawrence

Matrix Product States for Inference in Discrete Probabilistic Models

Rasmus Bonnevie, Mikkel N. Schmidt

As You Like It: Localization via Paired Comparisons

Andrew K. Massimino, Mark A. Davenport

Mode-wise Tensor Decompositions: Multi-dimensional Generalizations of CUR Decompositions

HanQin Cai, Keaton Hamm, Longxiu Huang, Deanna Needell

mlr3pipelines - Flexible Machine Learning Pipelines in R

Martin Binder, Florian Pfisterer, Michel Lang, Lennart Schneider, Lars Kotthoff

Benchmarking Unsupervised Object Representations for Video Sequences

Marissa A. Weis, Kashyap Chitta, Yash Sharma, Wieland Brendel, Matthias Bethge

A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning

Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo, Jan Peters

Alibi Explain: Algorithms for Explaining Machine Learning Models

Janis Klaise, Arnaud Van Looveren, Giovanni Vacanti, Alexandru Coca

Improved Shrinkage Prediction under a Spiked Covariance Structure

Trambak Banerjee, Gourab Mukherjee, Debashis Paul

A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration

Yuetian Luo, Garvesh Raskutti, Ming Yuan, Anru R. Zhang

Conditional independences and causal relations implied by sets of equations

Tineke Blom, Mirthe M. van Diepen, Joris M. Mooij

Prediction Under Latent Factor Regression: Adaptive PCR, Interpolating Predictors and Beyond

Xin Bing, Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp

Locally Private k-Means Clustering

Uri Stemmer

Doubly infinite residual neural networks: a diffusion process approach

Stefano Peluchetti, Stefano Favaro

Achieving Fairness in the Stochastic Multi-Armed Bandit Problem

Vishakha Patil, Ganesh Ghalme, Vineet Nair, Y. Narahari

Replica Exchange for Non-Convex Optimization

Jing Dong, Xin T. Tong

Unlinked Monotone Regression

Fadoua Balabdaoui, Charles R. Doss, Cécile Durot

Optimal Rates of Distributed Regression with Imperfect Kernels

Hongwei Sun, Qiang Wu

Black-Box Reductions for Zeroth-Order Gradient Algorithms to Achieve Lower Query Complexity

Bin Gu, Xiyuan Wei, Shangqian Gao, Ziran Xiong, Cheng Deng

First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems

Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang

Asymptotic Normality, Concentration, and Coverage of Generalized Posteriors

Jeffrey W. Miller

Estimation and Optimization of Composite Outcomes

Daniel J. Luckett, Eric B. Laber, Siyeon Kim, Michael R. Kosorok

The ensmallen library for flexible numerical optimization

Ryan R. Curtin, Marcus Edel, Rahul Ganesh Prabhu, Suryoday Basak, Zhihao Lou

Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning

Charles H. Martin, Michael W. Mahoney

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

PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review

Ivan Stelmakh, Nihar Shah, Aarti Singh

Counterfactual Mean Embeddings

Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, Sanparith Marukatat

MetaGrad: Adaptation using Multiple Learning Rates in Online Learning

Tim van Erven, Wouter M. Koolen, Dirk van der Hoeven

Are We Forgetting about Compositional Optimisers in Bayesian Optimisation?

Antoine Grosnit, Alexander I. Cowen-Rivers, Rasul Tutunov, Ryan-Rhys Griffiths, Jun Wang

When Does Gradient Descent with Logistic Loss Find Interpolating Two-Layer Networks?

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

Information criteria for non-normalized models

Takeru Matsuda, Masatoshi Uehara, Aapo Hyvärinen

The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks

Takuo Matsubara, Chris J. Oates, François-Xavier Briol

A Greedy Algorithm for Quantizing Neural Networks

Eric Lybrand, Rayan Saab

What Causes the Test Error? Going Beyond Bias-Variance via ANOVA

Licong Lin, Edgar Dobriban

Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data

Yikun Zhang, Yen-Chi Chen

Factorization Machines with Regularization for Sparse Feature Interactions

Kyohei Atarashi, Satoshi Oyama, Masahito Kurihara

Hardness of Identity Testing for Restricted Boltzmann Machines and Potts models

Antonio Blanca, Zongchen Chen, Daniel Stefankovic, Eric Vigoda

Universal consistency and rates of convergence of multiclass prototype algorithms in metric spaces

László Györfi, Roi Weiss

Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent

Tian Tong, Cong Ma, Yuejie Chi

Hyperparameter Optimization via Sequential Uniform Designs

Zebin Yang, Aijun Zhang

Statistical guarantees for local graph clustering

Wooseok Ha, Kimon Fountoulakis, Michael W. Mahoney

Optimal Minimax Variable Selection for Large-Scale Matrix Linear Regression Model

Meiling Hao, Lianqiang Qu, Dehan Kong, Liuquan Sun, Hongtu Zhu

Nonparametric Modeling of Higher-Order Interactions via Hypergraphons

Krishnakumar Balasubramanian

On efficient multilevel Clustering via Wasserstein distances

Viet Huynh, nhật Hồ, Nhan Dam, Xuanlong Nguyen, Mikhail Yurochkin

Individual Fairness in Hindsight

Swati Gupta, Vijay Kamble

Non-attracting Regions of Local Minima in Deep and Wide Neural Networks

Henning Petzka, Cristian Sminchisescu

Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace

Jesús Arroyo, Avanti Athreya, Joshua Cape, Guodong Chen, Carey E. Priebe

Pseudo-Marginal Hamiltonian Monte Carlo

Johan Alenlöv, Arnoud Doucet, Fredrik Lindsten

Generalization Properties of hyper-RKHS and its Applications

Fanghui Liu, Lei Shi, Xiaolin Huang, jie Yang, Johan A.K. Suykens

Hoeffding's Inequality for General Markov Chains and Its Applications to Statistical Learning

Jianqing Fan, Bai Jiang, Qiang Sun

An algorithmic view of L2 regularization and some path-following algorithms

Yunzhang Zhu, Renxiong Liu

Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model

Tong Wang, Qihang Lin

Implicit Langevin Algorithms for Sampling From Log-concave Densities

Liam Hodgkinson, Robert Salomone, Fred Roosta

Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives

Antoine Dedieu, Hussein Hazimeh, Rahul Mazumder

An Inertial Newton Algorithm for Deep Learning

Camille Castera, Jérôme Bolte, Cédric Févotte, Edouard Pauwels

A Contextual Bandit Bake-off

Alberto Bietti, Alekh Agarwal, John Langford

Locally Differentially-Private Randomized Response for Discrete Distribution Learning

Adriano Pastore, Michael Gastpar

MushroomRL: Simplifying Reinforcement Learning Research

Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters

Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes

Steve Hanneke

Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime

Niladri S. Chatterji, Philip M. Long

Optimal Bounds between f-Divergences and Integral Probability Metrics

Rohit Agrawal, Thibaut Horel

LassoNet: A Neural Network with Feature Sparsity

Ismael Lemhadri, Feng Ruan, Louis Abraham, Robert Tibshirani

Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints

Molei Liu, Yin Xia, Kelly Cho, Tianxi Cai

Bandit Convex Optimization in Non-stationary Environments

Peng Zhao, Guanghui Wang, Lijun Zhang, Zhi-Hua Zhou

A flexible model-free prediction-based framework for feature ranking

Jingyi Jessica Li, Yiling Elaine Chen, Xin Tong

Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness

George Wynne, François-Xavier Briol, Mark Girolami

Sparse Convex Optimization via Adaptively Regularized Hard Thresholding

Kyriakos Axiotis, Maxim Sviridenko

Langevin Dynamics for Adaptive Inverse Reinforcement Learning of Stochastic Gradient Algorithms

Vikram Krishnamurthy, George Yin

Empirical Bayes Matrix Factorization

Wei Wang, Matthew Stephens

Some Theoretical Insights into Wasserstein GANs

Gérard Biau, Maxime Sangnier, Ugo Tanielian

A General Framework for Adversarial Label Learning

Chidubem Arachie, Bert Huang

Strong Consistency, Graph Laplacians, and the Stochastic Block Model

Shaofeng Deng, Shuyang Ling, Thomas Strohmer

An Importance Weighted Feature Selection Stability Measure

Victor Hamer, Pierre Dupont

Stochastic Proximal Methods for Non-Smooth Non-Convex Constrained Sparse Optimization

Michael R. Metel, Akiko Takeda

NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization

Ali Ramezani-Kebrya, Fartash Faghri, Ilya Markov, Vitalii Aksenov, Dan Alistarh

A Lyapunov Analysis of Accelerated Methods in Optimization

Ashia C. Wilson, Ben Recht, Michael I. Jordan

L-SVRG and L-Katyusha with Arbitrary Sampling

Xun Qian, Zheng Qu, Peter Richtarik

Non-parametric Quantile Regression via the K-NN Fused Lasso

Steven Siwei Ye, OSCAR HERNAN MADRID PADILLA

River: machine learning for streaming data in Python

Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphaël Sourty

mvlearn: Multiview Machine Learning in Python

Ronan Perry, Gavin Mischler, Richard Guo, Theodore Lee, Alexander Chang

Towards a Unified Analysis of Random Fourier Features

Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic

Beyond English-Centric Multilingual Machine Translation

Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky

Online stochastic gradient descent on non-convex losses from high-dimensional inference

Gerard Ben Arous, Reza Gheissari, Aukosh Jagannath

Pathwise Conditioning of Gaussian Processes

James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth

Explaining Explanations: Axiomatic Feature Interactions for Deep Networks

Joseph D. Janizek, Pascal Sturmfels, Su-in Lee

A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints

Guodong Zhang, Xuchan Bao, Laurent Lessard, Roger Grosse

Learning a High-dimensional Linear Structural Equation Model via l1-Regularized Regression

Gunwoong Park, Sang Jun Moon, Sion Park, Jong-June Jeon

LocalGAN: Modeling Local Distributions for Adversarial Response Generation

Baoxun Wang, Zhen Xu, Huan Zhang, Kexin Qiu, Deyuan Zhang

OpenML-Python: an extensible Python API for OpenML

Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik

Adaptive estimation of nonparametric functionals

Lin Liu, Rajarshi Mukherjee, James M. Robins, Eric Tchetgen Tchetgen

On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift

Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan

Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach

Alberto Maria Metelli, Matteo Pirotta, Daniele Calandriello, Marcello Restelli

Guided Visual Exploration of Relations in Data Sets

Kai Puolamäki, Emilia Oikarinen, Andreas Henelius

Histogram Transform Ensembles for Large-scale Regression

Hanyuan Hang, Zhouchen Lin, Xiaoyu Liu, Hongwei Wen

Consistent Semi-Supervised Graph Regularization for High Dimensional Data

Xiaoyi MAI, Romain Couillet

Flexible Signal Denoising via Flexible Empirical Bayes Shrinkage

Zhengrong Xing, Peter Carbonetto, Matthew Stephens

NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation

Anastasis Kratsios, Cody Hyndman

Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler

Tingting Zhao, Alexandre Bouchard-côté