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Journal of Machine Learning Research

The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online.

JMLR has a commitment to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1533-7928) immediately upon receipt. Until the end of 2004, paper volumes (ISSN 1532-4435) were published 8 times annually and sold to libraries and individuals by the MIT Press. Paper volumes (ISSN 1532-4435) are now published and sold by Microtome Publishing.


Latest papers

On Universal Approximation and Error Bounds for Fourier Neural Operators
Nikola Kovachki, Samuel Lanthaler, Siddhartha Mishra, 2021.
[abs][pdf][bib]

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, 2021.
[abs][pdf][bib]      [code]

A Theory of the Risk for Optimization with Relaxation and its Application to Support Vector Machines
Marco C. Campi, Simone Garatti, 2021.
[abs][pdf][bib]

V-statistics and Variance Estimation
Zhengze Zhou, Lucas Mentch, Giles Hooker, 2021.
[abs][pdf][bib]      [code]

An Online Sequential Test for Qualitative Treatment Effects
Chengchun Shi, Shikai Luo, Hongtu Zhu, Rui Song, 2021.
[abs][pdf][bib]

Double Generative Adversarial Networks for Conditional Independence Testing
Chengchun Shi, Tianlin Xu, Wicher Bergsma, Lexin Li, 2021.
[abs][pdf][bib]

Linear Bandits on Uniformly Convex Sets
Thomas Kerdreux, Christophe Roux, Alexandre d'Aspremont, Sebastian Pokutta, 2021.
[abs][pdf][bib]

Non-linear, Sparse Dimensionality Reduction via Path Lasso Penalized Autoencoders
Oskar Allerbo, Rebecka Jörnsten, 2021.
[abs][pdf][bib]      [code]

LDLE: Low Distortion Local Eigenmaps
Dhruv Kohli, Alexander Cloninger, Gal Mishne, 2021.
[abs][pdf][bib]      [code]

Contrastive Estimation Reveals Topic Posterior Information to Linear Models
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, 2021.
[abs][pdf][bib]

Graph Matching with Partially-Correct Seeds
Liren Yu, Jiaming Xu, Xiaojun Lin, 2021.
[abs][pdf][bib]      [code]

Fast Learning for Renewal Optimization in Online Task Scheduling
Michael J. Neely, 2021.
[abs][pdf][bib]

Multilevel Monte Carlo Variational Inference
Masahiro Fujisawa, Issei Sato, 2021.
[abs][pdf][bib]

Gaussian Approximation for Bias Reduction in Q-Learning
Carlo D'Eramo, Andrea Cini, Alessandro Nuara, Matteo Pirotta, Cesare Alippi, Jan Peters, Marcello Restelli, 2021.
[abs][pdf][bib]

Estimating the Lasso's Effective Noise
Johannes Lederer, Michael Vogt, 2021.
[abs][pdf][bib]

Partial Policy Iteration for L1-Robust Markov Decision Processes
Chin Pang Ho, Marek Petrik, Wolfram Wiesemann, 2021.
[abs][pdf][bib]      [code]

Simultaneous Change Point Inference and Structure Recovery for High Dimensional Gaussian Graphical Models
Bin Liu, Xinsheng Zhang, Yufeng Liu, 2021.
[abs][pdf][bib]

On the Hardness of Robust Classification
Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell, 2021.
[abs][pdf][bib]

Transferability of Spectral Graph Convolutional Neural Networks
Ron Levie, Wei Huang, Lorenzo Bucci, Michael Bronstein, Gitta Kutyniok, 2021.
[abs][pdf][bib]

Nonparametric Continuous Sensor Registration
William Clark, Maani Ghaffari, Anthony Bloch, 2021.
[abs][pdf][bib]      [code]

Further results on latent discourse models and word embeddings
Sammy Khalife, Douglas Gonçalves, Youssef Allouah, Leo Liberti, 2021.
[abs][pdf][bib]

CAT: Compression-Aware Training for bandwidth reduction
Chaim Baskin, Brian Chmiel, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, Avi Mendelson, 2021.
[abs][pdf][bib]      [code]

Stable-Baselines3: Reliable Reinforcement Learning Implementations
Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, Noah Dormann, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Reproducing kernel Hilbert C*-module and kernel mean embeddings
Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Takeshi Katsura, Yoshinobu Kawahara, 2021.
[abs][pdf][bib]

Learning Bayesian Networks from Ordinal Data
Xiang Ge Luo, Giusi Moffa, Jack Kuipers, 2021.
[abs][pdf][bib]      [code]

Exact Asymptotics for Linear Quadratic Adaptive Control
Feicheng Wang, Lucas Janson, 2021.
[abs][pdf][bib]      [code]

Regularized spectral methods for clustering signed networks
Mihai Cucuringu, Apoorv Vikram Singh, Déborah Sulem, Hemant Tyagi, 2021.
[abs][pdf][bib]

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, 2021.
[abs][pdf][bib]

Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning
Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang, 2021.
[abs][pdf][bib]

Domain adaptation under structural causal models
Yuansi Chen, Peter Bühlmann, 2021.
[abs][pdf][bib]      [code]

Learning Strategies in Decentralized Matching Markets under Uncertain Preferences
Xiaowu Dai, Michael I. Jordan, 2021.
[abs][pdf][bib]

ROOTS: Object-Centric Representation and Rendering of 3D Scenes
Chang Chen, Fei Deng, Sungjin Ahn, 2021.
[abs][pdf][bib]

Optimized Score Transformation for Consistent Fair Classification
Dennis Wei, Karthikeyan Natesan Ramamurthy, Flavio P. Calmon, 2021.
[abs][pdf][bib]

Estimating Uncertainty Intervals from Collaborating Networks
Tianhui Zhou, Yitong Li, Yuan Wu, David Carlson, 2021.
[abs][pdf][bib]

Model Linkage Selection for Cooperative Learning
Jiaying Zhou, Jie Ding, Kean Ming Tan, Vahid Tarokh, 2021.
[abs][pdf][bib]

Adversarial Monte Carlo Meta-Learning of Optimal Prediction Procedures
Alex Luedtke, Incheoul Chung, Oleg Sofrygin, 2021.
[abs][pdf][bib]      [code]

Inference for the Case Probability in High-dimensional Logistic Regression
Zijian Guo, Prabrisha Rakshit, Daniel S. Herman, Jinbo Chen, 2021.
[abs][pdf][bib]

Bifurcation Spiking Neural Network
Shao-Qun Zhang, Zhao-Yu Zhang, Zhi-Hua Zhou, 2021.
[abs][pdf][bib]

Batch greedy maximization of non-submodular functions: Guarantees and applications to experimental design
Jayanth Jagalur-Mohan, Youssef Marzouk, 2021.
[abs][pdf][bib]

Tractable Approximate Gaussian Inference for Bayesian Neural Networks
James-A. Goulet, Luong Ha Nguyen, Saeid Amiri, 2021.
[abs][pdf][bib]      [code]

Bayesian time-aligned factor analysis of paired multivariate time series
Arkaprava Roy, Jana Schaich Borg, David B Dunson, 2021.
[abs][pdf][bib]

On the Riemannian Search for Eigenvector Computation
Zhiqiang Xu, Ping Li, 2021.
[abs][pdf][bib]

Statistically and Computationally Efficient Change Point Localization in Regression Settings
Daren Wang, Zifeng Zhao, Kevin Z. Lin, Rebecca Willett, 2021.
[abs][pdf][bib]

Statistical Guarantees for Local Spectral Clustering on Random Neighborhood Graphs
Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani, 2021.
[abs][pdf][bib]

Mixture Martingales Revisited with Applications to Sequential Tests and Confidence Intervals
Emilie Kaufmann, Wouter M. Koolen, 2021.
[abs][pdf][bib]

On lp-hyperparameter Learning via Bilevel Nonsmooth Optimization
Takayuki Okuno, Akiko Takeda, Akihiro Kawana, Motokazu Watanabe, 2021.
[abs][pdf][bib]

Consistency of Gaussian Process Regression in Metric Spaces
Peter Koepernik, Florian Pfaff, 2021.
[abs][pdf][bib]

Quasi-Monte Carlo Quasi-Newton in Variational Bayes
Sifan Liu, Art B. Owen, 2021.
[abs][pdf][bib]

Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations
Jesus Maria Sanz-Serna, Konstantinos C. Zygalakis, 2021.
[abs][pdf][bib]

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, 2021.
[abs][pdf][bib]      [code]

DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Haiyang Yu, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora M Oztekin, Xuan Zhang, Shuiwang Ji, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo
Mert Gürbüzbalaban, Xuefeng Gao, Yuanhan Hu, Lingjiong Zhu, 2021.
[abs][pdf][bib]

DeEPCA: Decentralized Exact PCA with Linear Convergence Rate
Haishan Ye, Tong Zhang, 2021.
[abs][pdf][bib]

Consensus-Based Optimization on the Sphere: Convergence to Global Minimizers and Machine Learning
Massimo Fornasier, Lorenzo Pareschi, Hui Huang, Philippe Sünnen, 2021.
[abs][pdf][bib]      [code]

Expanding Boundaries of Gap Safe Screening
Cassio F. Dantas, Emmanuel Soubies, Cédric Févotte, 2021.
[abs][pdf][bib]      [code]

GIBBON: General-purpose Information-Based Bayesian Optimisation
Henry B. Moss, David S. Leslie, Javier Gonzalez, Paul Rayson, 2021.
[abs][pdf][bib]

A general linear-time inference method for Gaussian Processes on one dimension
Jackson Loper, David Blei, John P. Cunningham, Liam Paninski, 2021.
[abs][pdf][bib]

A Generalised Linear Model Framework for β-Variational Autoencoders based on Exponential Dispersion Families
Robert Sicks, Ralf Korn, Stefanie Schwaar, 2021.
[abs][pdf][bib]

Probabilistic Iterative Methods for Linear Systems
Jon Cockayne, Ilse C.F. Ipsen, Chris J. Oates, Tim W. Reid, 2021.
[abs][pdf][bib]      [code]

sklvq: Scikit Learning Vector Quantization
Rick van Veen, Michael Biehl, Gert-Jan de Vries, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Learning with semi-definite programming: statistical bounds based on fixed point analysis and excess risk curvature
Stéphane Chrétien, Mihai Cucuringu, Guillaume Lecué, Lucie Neirac, 2021.
[abs][pdf][bib]

Convolutional Neural Networks Are Not Invariant to Translation, but They Can Learn to Be
Valerio Biscione, Jeffrey S. Bowers, 2021.
[abs][pdf][bib]

How Well Generative Adversarial Networks Learn Distributions
Tengyuan Liang, 2021.
[abs][pdf][bib]

Tighter Risk Certificates for Neural Networks
María Pérez-Ortiz, Omar Risvaplata, John Shawe-Taylor, Csaba Szepesvári, 2021.
[abs][pdf][bib]      [code]

FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection
Yang Liu, Tao Fan, Tianjian Chen, Qian Xu, Qiang Yang, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Representer Theorems in Banach Spaces: Minimum Norm Interpolation, Regularized Learning and Semi-Discrete Inverse Problems
Rui Wang, Yuesheng Xu, 2021.
[abs][pdf][bib]

Bayesian Distance Clustering
Leo L. Duan, David B. Dunson, 2021.
[abs][pdf][bib]      [code]

Stochastic Online Optimization using Kalman Recursion
Joseph de Vilmarest, Olivier Wintenberger, 2021.
[abs][pdf][bib]

Classification vs regression in overparameterized regimes: Does the loss function matter?
Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel Hsu, Anant Sahai, 2021.
[abs][pdf][bib]

A Bayes-Optimal View on Adversarial Examples
Eitan Richardson, Yair Weiss, 2021.
[abs][pdf][bib]      [code]

Shape-Enforcing Operators for Generic Point and Interval Estimators of Functions
Xi Chen, Victor Chernozhukov, Ivan Fernandez-Val, Scott Kostyshak, Ye Luo, 2021.
[abs][pdf][bib]

Soft Tensor Regression
Georgia Papadogeorgou, Zhengwu Zhang, David B. Dunson, 2021.
[abs][pdf][bib]

Thompson Sampling Algorithms for Cascading Bandits
Zixin Zhong, Wang Chi Chueng, Vincent Y. F. Tan, 2021.
[abs][pdf][bib]

A Unified Framework for Spectral Clustering in Sparse Graphs
Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay, 2021.
[abs][pdf][bib]

Context-dependent Networks in Multivariate Time Series: Models, Methods, and Risk Bounds in High Dimensions
Lili Zheng, Garvesh Raskutti, Rebecca Willett, Benjamin Mark, 2021.
[abs][pdf][bib]

TensorHive: Management of Exclusive GPU Access for Distributed Machine Learning Workloads
Paweł Rościszewski, Michał Martyniak, Filip Schodowski, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python
Hubert Baniecki, Wojciech Kretowicz, Piotr Piątyszek, Jakub Wiśniewski, Przemysław Biecek, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Cooperative SGD: A Unified Framework for the Design and Analysis of Local-Update SGD Algorithms
Jianyu Wang, Gauri Joshi, 2021.
[abs][pdf][bib]

Convex Geometry and Duality of Over-parameterized Neural Networks
Tolga Ergen, Mert Pilanci, 2021.
[abs][pdf][bib]

Bandit Learning in Decentralized Matching Markets
Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan, 2021.
[abs][pdf][bib]

Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks
Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla, 2021.
[abs][pdf][bib]      [code]

Explaining by Removing: A Unified Framework for Model Explanation
Ian Covert, Scott Lundberg, Su-In Lee, 2021.
[abs][pdf][bib]      [code]

Oblivious Data for Fairness with Kernels
Steffen Grünewälder, Azadeh Khaleghi, 2021.
[abs][pdf][bib]      [code]

A Unified Convergence Analysis for Shuffling-Type Gradient Methods
Lam M. Nguyen, Quoc Tran-Dinh, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk, 2021.
[abs][pdf][bib]

Hamilton-Jacobi Deep Q-Learning for Deterministic Continuous-Time Systems with Lipschitz Continuous Controls
Jeongho Kim, Jaeuk Shin, Insoon Yang, 2021.
[abs][pdf][bib]      [code]

Langevin Monte Carlo: random coordinate descent and variance reduction
Zhiyan Ding, Qin Li, 2021.
[abs][pdf][bib]

Failures of Model-dependent Generalization Bounds for Least-norm Interpolation
Peter L. Bartlett, Philip M. Long, 2021.
[abs][pdf][bib]

Learning partial correlation graphs and graphical models by covariance queries
Gábor Lugosi, Jakub Truszkowski, Vasiliki Velona, Piotr Zwiernik, 2021.
[abs][pdf][bib]

Interpretable Deep Generative Recommendation Models
Huafeng Liu, Liping Jing, Jingxuan Wen, Pengyu Xu, Jiaqi Wang, Jian Yu, Michael K. Ng, 2021.
[abs][pdf][bib]

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, 2021.
[abs][pdf][bib]      [code]

Refined approachability algorithms and application to regret minimization with global costs
Joon Kwon, 2021.
[abs][pdf][bib]

On ADMM in Deep Learning: Convergence and Saturation-Avoidance
Jinshan Zeng, Shao-Bo Lin, Yuan Yao, Ding-Xuan Zhou, 2021.
[abs][pdf][bib]

Integrated Principal Components Analysis
Tiffany M. Tang, Genevera I. Allen, 2021.
[abs][pdf][bib]      [code]

Particle-Gibbs Sampling for Bayesian Feature Allocation Models
Alexandre Bouchard-Côté, Andrew Roth, 2021.
[abs][pdf][bib]      [code]

COKE: Communication-Censored Decentralized Kernel Learning
Ping Xu, Yue Wang, Xiang Chen, Zhi Tian, 2021.
[abs][pdf][bib]

Learning Laplacian Matrix from Graph Signals with Sparse Spectral Representation
Pierre Humbert, Batiste Le Bars, Laurent Oudre, Argyris Kalogeratos, Nicolas Vayatis, 2021.
[abs][pdf][bib]      [code]

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, 2021.
[abs][pdf][bib]

Sparse Popularity Adjusted Stochastic Block Model
Majid Noroozi, Marianna Pensky, Ramchandra Rimal, 2021.
[abs][pdf][bib]

Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models
Fei Wang, Ling Zhou, Lu Tang, Peter X.K. Song, 2021.
[abs][pdf][bib]

On the Estimation of Network Complexity: Dimension of Graphons
Yann Issartel, 2021.
[abs][pdf][bib]

Collusion Detection and Ground Truth Inference in Crowdsourcing for Labeling Tasks
Changyue Song, Kaibo Liu, Xi Zhang, 2021.
[abs][pdf][bib]

One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them
Saber Salehkaleybar, Arsalan Sharifnassab, S. Jamaloddin Golestani, 2021.
[abs][pdf][bib]      [code]

Differentially Private Regression and Classification with Sparse Gaussian Processes
Michael Thomas Smith, Mauricio A. Alvarez, Neil D. Lawrence, 2021.
[abs][pdf][bib]      [code]

Matrix Product States for Inference in Discrete Probabilistic Models
Rasmus Bonnevie, Mikkel N. Schmidt, 2021.
[abs][pdf][bib]

As You Like It: Localization via Paired Comparisons
Andrew K. Massimino, Mark A. Davenport, 2021.
[abs][pdf][bib]

Mode-wise Tensor Decompositions: Multi-dimensional Generalizations of CUR Decompositions
HanQin Cai, Keaton Hamm, Longxiu Huang, Deanna Needell, 2021.
[abs][pdf][bib]      [code]

mlr3pipelines - Flexible Machine Learning Pipelines in R
Martin Binder, Florian Pfisterer, Michel Lang, Lennart Schneider, Lars Kotthoff, Bernd Bischl, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Benchmarking Unsupervised Object Representations for Video Sequences
Marissa A. Weis, Kashyap Chitta, Yash Sharma, Wieland Brendel, Matthias Bethge, Andreas Geiger, Alexander S. Ecker, 2021.
[abs][pdf][bib]      [code]

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, 2021.
[abs][pdf][bib]      [code]

Alibi Explain: Algorithms for Explaining Machine Learning Models
Janis Klaise, Arnaud Van Looveren, Giovanni Vacanti, Alexandru Coca, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Improved Shrinkage Prediction under a Spiked Covariance Structure
Trambak Banerjee, Gourab Mukherjee, Debashis Paul, 2021.
[abs][pdf][bib]

A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration
Yuetian Luo, Garvesh Raskutti, Ming Yuan, Anru R. Zhang, 2021.
[abs][pdf][bib]

Conditional independences and causal relations implied by sets of equations
Tineke Blom, Mirthe M. van Diepen, Joris M. Mooij, 2021.
[abs][pdf][bib]

Prediction Under Latent Factor Regression: Adaptive PCR, Interpolating Predictors and Beyond
Xin Bing, Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp, 2021.
[abs][pdf][bib]

Locally Private k-Means Clustering
Uri Stemmer, 2021.
[abs][pdf][bib]

Doubly infinite residual neural networks: a diffusion process approach
Stefano Peluchetti, Stefano Favaro, 2021.
[abs][pdf][bib]

Achieving Fairness in the Stochastic Multi-Armed Bandit Problem
Vishakha Patil, Ganesh Ghalme, Vineet Nair, Y. Narahari, 2021.
[abs][pdf][bib]

Replica Exchange for Non-Convex Optimization
Jing Dong, Xin T. Tong, 2021.
[abs][pdf][bib]

Unlinked Monotone Regression
Fadoua Balabdaoui, Charles R. Doss, Cécile Durot, 2021.
[abs][pdf][bib]

Optimal Rates of Distributed Regression with Imperfect Kernels
Hongwei Sun, Qiang Wu, 2021.
[abs][pdf][bib]

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, 2021.
[abs][pdf][bib]

First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems
Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang, 2021.
[abs][pdf][bib]

Asymptotic Normality, Concentration, and Coverage of Generalized Posteriors
Jeffrey W. Miller, 2021.
[abs][pdf][bib]

Estimation and Optimization of Composite Outcomes
Daniel J. Luckett, Eric B. Laber, Siyeon Kim, Michael R. Kosorok, 2021.
[abs][pdf][bib]

The ensmallen library for flexible numerical optimization
Ryan R. Curtin, Marcus Edel, Rahul Ganesh Prabhu, Suryoday Basak, Zhihao Lou, Conrad Sanderson, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning
Charles H. Martin, Michael W. Mahoney, 2021.
[abs][pdf][bib]

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, 2021.
[abs][pdf][bib]

PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review
Ivan Stelmakh, Nihar Shah, Aarti Singh, 2021.
[abs][pdf][bib]

Counterfactual Mean Embeddings
Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, Sanparith Marukatat, 2021.
[abs][pdf][bib]      [code]

MetaGrad: Adaptation using Multiple Learning Rates in Online Learning
Tim van Erven, Wouter M. Koolen, Dirk van der Hoeven, 2021.
[abs][pdf][bib]      [code]

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, 2021.
[abs][pdf][bib]      [code]

When Does Gradient Descent with Logistic Loss Find Interpolating Two-Layer Networks?
Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett, 2021.
[abs][pdf][bib]

Information criteria for non-normalized models
Takeru Matsuda, Masatoshi Uehara, Aapo Hyvarinen, 2021.
[abs][pdf][bib]

The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks
Takuo Matsubara, Chris J. Oates, François-Xavier Briol, 2021.
[abs][pdf][bib]

A Greedy Algorithm for Quantizing Neural Networks
Eric Lybrand, Rayan Saab, 2021.
[abs][pdf][bib]      [code]

What Causes the Test Error? Going Beyond Bias-Variance via ANOVA
Licong Lin, Edgar Dobriban, 2021.
[abs][pdf][bib]

Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data
Yikun Zhang, Yen-Chi Chen, 2021.
[abs][pdf][bib]      [code]

Factorization Machines with Regularization for Sparse Feature Interactions
Kyohei Atarashi, Satoshi Oyama, Masahito Kurihara, 2021.
[abs][pdf][bib]

Hardness of Identity Testing for Restricted Boltzmann Machines and Potts models
Antonio Blanca, Zongchen Chen, Daniel Štefankovič, Eric Vigoda, 2021.
[abs][pdf][bib]

Universal consistency and rates of convergence of multiclass prototype algorithms in metric spaces
László Györfi, Roi Weiss, 2021.
[abs][pdf][bib]

Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent
Tian Tong, Cong Ma, Yuejie Chi, 2021.
[abs][pdf][bib]      [code]

Hyperparameter Optimization via Sequential Uniform Designs
Zebin Yang, Aijun Zhang, 2021.
[abs][pdf][bib]      [code]

Statistical guarantees for local graph clustering
Wooseok Ha, Kimon Fountoulakis, Michael W. Mahoney, 2021.
[abs][pdf][bib]

Optimal Minimax Variable Selection for Large-Scale Matrix Linear Regression Model
Meiling Hao, Lianqiang Qu, Dehan Kong, Liuquan Sun, Hongtu Zhu, 2021.
[abs][pdf][bib]

Nonparametric Modeling of Higher-Order Interactions via Hypergraphons
Krishnakumar Balasubramanian, 2021.
[abs][pdf][bib]

On efficient multilevel Clustering via Wasserstein distances
Viet Huynh, Nhat Ho, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, Dinh Phung, 2021.
[abs][pdf][bib]

Individual Fairness in Hindsight
Swati Gupta, Vijay Kamble, 2021.
[abs][pdf][bib]

Non-attracting Regions of Local Minima in Deep and Wide Neural Networks
Henning Petzka, Cristian Sminchisescu, 2021.
[abs][pdf][bib]

Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace
Jesús Arroyo, Avanti Athreya, Joshua Cape, Guodong Chen, Carey E. Priebe, Joshua T. Vogelstein, 2021.
[abs][pdf][bib]      [code]

Pseudo-Marginal Hamiltonian Monte Carlo
Johan Alenlöv, Arnoud Doucet, Fredrik Lindsten, 2021.
[abs][pdf][bib]

Generalization Properties of hyper-RKHS and its Applications
Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A.K. Suykens, 2021.
[abs][pdf][bib]

Hoeffding's Inequality for General Markov Chains and Its Applications to Statistical Learning
Jianqing Fan, Bai Jiang, Qiang Sun, 2021.
[abs][pdf][bib]

An algorithmic view of L2 regularization and some path-following algorithms
Yunzhang Zhu, Renxiong Liu, 2021.
[abs][pdf][bib]

Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model
Tong Wang, Qihang Lin, 2021.
[abs][pdf][bib]      [code]

Implicit Langevin Algorithms for Sampling From Log-concave Densities
Liam Hodgkinson, Robert Salomone, Fred Roosta, 2021.
[abs][pdf][bib]

Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives
Antoine Dedieu, Hussein Hazimeh, Rahul Mazumder, 2021.
[abs][pdf][bib]      [code]

An Inertial Newton Algorithm for Deep Learning
Camille Castera, Jérôme Bolte, Cédric Févotte, Edouard Pauwels, 2021.
[abs][pdf][bib]      [code]

A Contextual Bandit Bake-off
Alberto Bietti, Alekh Agarwal, John Langford, 2021.
[abs][pdf][bib]      [code]

Locally Differentially-Private Randomized Response for Discrete Distribution Learning
Adriano Pastore, Michael Gastpar, 2021.
[abs][pdf][bib]

MushroomRL: Simplifying Reinforcement Learning Research
Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes
Steve Hanneke, 2021.
[abs][pdf][bib]

Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime
Niladri S. Chatterji, Philip M. Long, 2021.
[abs][pdf][bib]

Optimal Bounds between f-Divergences and Integral Probability Metrics
Rohit Agrawal, Thibaut Horel, 2021.
[abs][pdf][bib]

LassoNet: A Neural Network with Feature Sparsity
Ismael Lemhadri, Feng Ruan, Louis Abraham, Robert Tibshirani, 2021.
[abs][pdf][bib]      [code]

Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints
Molei Liu, Yin Xia, Kelly Cho, Tianxi Cai, 2021.
[abs][pdf][bib]

Bandit Convex Optimization in Non-stationary Environments
Peng Zhao, Guanghui Wang, Lijun Zhang, Zhi-Hua Zhou, 2021.
[abs][pdf][bib]

A flexible model-free prediction-based framework for feature ranking
Jingyi Jessica Li, Yiling Elaine Chen, Xin Tong, 2021.
[abs][pdf][bib]      [code]

Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness
George Wynne, François-Xavier Briol, Mark Girolami, 2021.
[abs][pdf][bib]

Sparse Convex Optimization via Adaptively Regularized Hard Thresholding
Kyriakos Axiotis, Maxim Sviridenko, 2021.
[abs][pdf][bib]

Langevin Dynamics for Adaptive Inverse Reinforcement Learning of Stochastic Gradient Algorithms
Vikram Krishnamurthy, George Yin, 2021.
[abs][pdf][bib]

Empirical Bayes Matrix Factorization
Wei Wang, Matthew Stephens, 2021.
[abs][pdf][bib]      [code]

Some Theoretical Insights into Wasserstein GANs
Gérard Biau, Maxime Sangnier, Ugo Tanielian, 2021.
[abs][pdf][bib]

A General Framework for Adversarial Label Learning
Chidubem Arachie, Bert Huang, 2021.
[abs][pdf][bib]      [code]

Strong Consistency, Graph Laplacians, and the Stochastic Block Model
Shaofeng Deng, Shuyang Ling, Thomas Strohmer, 2021.
[abs][pdf][bib]

An Importance Weighted Feature Selection Stability Measure
Victor Hamer, Pierre Dupont, 2021.
[abs][pdf][bib]

Stochastic Proximal Methods for Non-Smooth Non-Convex Constrained Sparse Optimization
Michael R. Metel, Akiko Takeda, 2021.
[abs][pdf][bib]

NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization
Ali Ramezani-Kebrya, Fartash Faghri, Ilya Markov, Vitalii Aksenov, Dan Alistarh, Daniel M. Roy, 2021.
[abs][pdf][bib]

A Lyapunov Analysis of Accelerated Methods in Optimization
Ashia C. Wilson, Ben Recht, Michael I. Jordan, 2021.
[abs][pdf][bib]

L-SVRG and L-Katyusha with Arbitrary Sampling
Xun Qian, Zheng Qu, Peter Richtárik, 2021.
[abs][pdf][bib]

Non-parametric Quantile Regression via the K-NN Fused Lasso
Steven Siwei Ye, Oscar Hernan Madrid Padilla, 2021.
[abs][pdf][bib]      [code]

River: machine learning for streaming data in Python
Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphael Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes, Jesse Read, Talel Abdessalem, Albert Bifet, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

mvlearn: Multiview Machine Learning in Python
Ronan Perry, Gavin Mischler, Richard Guo, Theodore Lee, Alexander Chang, Arman Koul, Cameron Franz, Hugo Richard, Iain Carmichael, Pierre Ablin, Alexandre Gramfort, Joshua T. Vogelstein, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Towards a Unified Analysis of Random Fourier Features
Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic, 2021.
[abs][pdf][bib]

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, 2021.
[abs][pdf][bib]      [code]

Online stochastic gradient descent on non-convex losses from high-dimensional inference
Gerard Ben Arous, Reza Gheissari, Aukosh Jagannath, 2021.
[abs][pdf][bib]

Pathwise Conditioning of Gaussian Processes
James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth, 2021.
[abs][pdf][bib]      [code]

Explaining Explanations: Axiomatic Feature Interactions for Deep Networks
Joseph D. Janizek, Pascal Sturmfels, Su-In Lee, 2021.
[abs][pdf][bib]      [code]

A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints
Guodong Zhang, Xuchan Bao, Laurent Lessard, Roger Grosse, 2021.
[abs][pdf][bib]      [code]

Learning a High-dimensional Linear Structural Equation Model via l1-Regularized Regression
Gunwoong Park, Sang Jun Moon, Sion Park, Jong-June Jeon, 2021.
[abs][pdf][bib]

LocalGAN: Modeling Local Distributions for Adversarial Response Generation
Baoxun Wang, Zhen Xu, Huan Zhang, Kexin Qiu, Deyuan Zhang, Chengjie Sun, 2021.
[abs][pdf][bib]      [code]

OpenML-Python: an extensible Python API for OpenML
Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Adaptive estimation of nonparametric functionals
Lin Liu, Rajarshi Mukherjee, James M. Robins, Eric Tchetgen Tchetgen, 2021.
[abs][pdf][bib]

On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift
Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan, 2021.
[abs][pdf][bib]

Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach
Alberto Maria Metelli, Matteo Pirotta, Daniele Calandriello, Marcello Restelli, 2021.
[abs][pdf][bib]

Guided Visual Exploration of Relations in Data Sets
Kai Puolamäki, Emilia Oikarinen, Andreas Henelius, 2021.
[abs][pdf][bib]      [code]

Histogram Transform Ensembles for Large-scale Regression
Hanyuan Hang, Zhouchen Lin, Xiaoyu Liu, Hongwei Wen, 2021.
[abs][pdf][bib]

Consistent Semi-Supervised Graph Regularization for High Dimensional Data
Xiaoyi Mai, Romain Couillet, 2021.
[abs][pdf][bib]

Flexible Signal Denoising via Flexible Empirical Bayes Shrinkage
Zhengrong Xing, Peter Carbonetto, Matthew Stephens, 2021.
[abs][pdf][bib]      [code]

NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation
Anastasis Kratsios, Cody Hyndman, 2021.
[abs][pdf][bib]      [code]

Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler
Tingting Zhao, Alexandre Bouchard-Côté, 2021.
[abs][pdf][bib]      [code]

Risk Bounds for Unsupervised Cross-Domain Mapping with IPMs
Tomer Galanti, Sagie Benaim, Lior Wolf, 2021.
[abs][pdf][bib]      [code]

Bayesian Text Classification and Summarization via A Class-Specified Topic Model
Feifei Wang, Junni L. Zhang, Yichao Li, Ke Deng, Jun S. Liu, 2021.
[abs][pdf][bib]

Edge Sampling Using Local Network Information
Can M. Le, 2021.
[abs][pdf][bib]

On Solving Probabilistic Linear Diophantine Equations
Patrick Kreitzberg, Oliver Serang, 2021.
[abs][pdf][bib]      [code]

Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
Andreas Damianou, Neil D. Lawrence, Carl Henrik Ek, 2021.
[abs][pdf][bib]

Gradient Methods Never Overfit On Separable Data
Ohad Shamir, 2021.
[abs][pdf][bib]

Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference
Jiyuan Tu, Weidong Liu, Xiaojun Mao, Xi Chen, 2021.
[abs][pdf][bib]

Statistical Query Lower Bounds for Tensor PCA
Rishabh Dudeja, Daniel Hsu, 2021.
[abs][pdf][bib]

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, 2021.
[abs][pdf][bib]

Knowing what You Know: valid and validated confidence sets in multiclass and multilabel prediction
Maxime Cauchois, Suyash Gupta, John C. Duchi, 2021.
[abs][pdf][bib]

Communication-Efficient Distributed Covariance Sketch, with Application to Distributed PCA
Zengfeng Huang, Xuemin Lin, Wenjie Zhang, Ying Zhang, 2021.
[abs][pdf][bib]

Is SGD a Bayesian sampler? Well, almost
Chris Mingard, Guillermo Valle-Pérez, Joar Skalse, Ard A. Louis, 2021.
[abs][pdf][bib]

POT: Python Optimal Transport
Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, Titouan Vayer, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

ChainerRL: A Deep Reinforcement Learning Library
Yasuhiro Fujita, Prabhat Nagarajan, Toshiki Kataoka, Takahiro Ishikawa, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Analyzing the discrepancy principle for kernelized spectral filter learning algorithms
Alain Celisse, Martin Wahl, 2021.
[abs][pdf][bib]

Attention is Turing-Complete
Jorge Pérez, Pablo Barceló, Javier Marinkovic, 2021.
[abs][pdf][bib]

Kernel Operations on the GPU, with Autodiff, without Memory Overflows
Benjamin Charlier, Jean Feydy, Joan Alexis Glaunès, François-David Collin, Ghislain Durif, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives
Michael Muehlebach, Michael I. Jordan, 2021.
[abs][pdf][bib]

Prediction against a limited adversary
Erhan Bayraktar, Ibrahim Ekren, Xin Zhang, 2021.
[abs][pdf][bib]

Phase Diagram for Two-layer ReLU Neural Networks at Infinite-width Limit
Tao Luo, Zhi-Qin John Xu, Zheng Ma, Yaoyu Zhang, 2021.
[abs][pdf][bib]      [code]

Testing Conditional Independence via Quantile Regression Based Partial Copulas
Lasse Petersen, Niels Richard Hansen, 2021.
[abs][pdf][bib]      [code]

Determining the Number of Communities in Degree-corrected Stochastic Block Models
Shujie Ma, Liangjun Su, Yichong Zhang, 2021.
[abs][pdf][bib]

Path Length Bounds for Gradient Descent and Flow
Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas, 2021.
[abs][pdf][bib]      [blog]

A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family
Trambak Banerjee, Qiang Liu, Gourab Mukherjee, Wengunag Sun, 2021.
[abs][pdf][bib]

Approximate Newton Methods
Haishan Ye, Luo Luo, Zhihua Zhang, 2021.
[abs][pdf][bib]

Dynamic Tensor Recommender Systems
Yanqing Zhang, Xuan Bi, Niansheng Tang, Annie Qu, 2021.
[abs][pdf][bib]

Sparse Tensor Additive Regression
Botao Hao, Boxiang Wang, Pengyuan Wang, Jingfei Zhang, Jian Yang, Will Wei Sun, 2021.
[abs][pdf][bib]

Geometric structure of graph Laplacian embeddings
Nicolás García Trillos, Franca Hoffmann, Bamdad Hosseini, 2021.
[abs][pdf][bib]

How to Gain on Power: Novel Conditional Independence Tests Based on Short Expansion of Conditional Mutual Information
Mariusz Kubkowski, Jan Mielniczuk, Paweł Teisseyre, 2021.
[abs][pdf][bib]

Stochastic Proximal AUC Maximization
Yunwen Lei, Yiming Ying, 2021.
[abs][pdf][bib]

A Distributed Method for Fitting Laplacian Regularized Stratified Models
Jonathan Tuck, Shane Barratt, Stephen Boyd, 2021.
[abs][pdf][bib]      [code]

Predictive Learning on Hidden Tree-Structured Ising Models
Konstantinos E. Nikolakakis, Dionysios S. Kalogerias, Anand D. Sarwate, 2021.
[abs][pdf][bib]      [code]

Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing Approach
Zhe Fei, Yi Li, 2021.
[abs][pdf][bib]      [code]

Normalizing Flows for Probabilistic Modeling and Inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan, 2021.
[abs][pdf][bib]

Incorporating Unlabeled Data into Distributionally Robust Learning
Charlie Frogner, Sebastian Claici, Edward Chien, Justin Solomon, 2021.
[abs][pdf][bib]

Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data
Minjie Wang, Genevera I. Allen, 2021.
[abs][pdf][bib]

GemBag: Group Estimation of Multiple Bayesian Graphical Models
Xinming Yang, Lingrui Gan, Naveen N. Narisetty, Feng Liang, 2021.
[abs][pdf][bib]

Subspace Clustering through Sub-Clusters
Weiwei Li, Jan Hannig, Sayan Mukherjee, 2021.
[abs][pdf][bib]

Sparse and Smooth Signal Estimation: Convexification of L0-Formulations
Alper Atamturk, Andres Gomez, Shaoning Han, 2021.
[abs][pdf][bib]

Projection-free Decentralized Online Learning for Submodular Maximization over Time-Varying Networks
Junlong Zhu, Qingtao Wu, Mingchuan Zhang, Ruijuan Zheng, Keqin Li, 2021.
[abs][pdf][bib]

Structure Learning of Undirected Graphical Models for Count Data
Nguyen Thi Kim Hue, Monica Chiogna, 2021.
[abs][pdf][bib]

From Low Probability to High Confidence in Stochastic Convex Optimization
Damek Davis, Dmitriy Drusvyatskiy, Lin Xiao, Junyu Zhang, 2021.
[abs][pdf][bib]

Optimal Feedback Law Recovery by Gradient-Augmented Sparse Polynomial Regression
Behzad Azmi, Dante Kalise, Karl Kunisch, 2021.
[abs][pdf][bib]

Understanding Recurrent Neural Networks Using Nonequilibrium Response Theory
Soon Hoe Lim, 2021.
[abs][pdf][bib]

Optimal Structured Principal Subspace Estimation: Metric Entropy and Minimax Rates
Tony Cai, Hongzhe Li, Rong Ma, 2021.
[abs][pdf][bib]

RaSE: Random Subspace Ensemble Classification
Ye Tian, Yang Feng, 2021.
[abs][pdf][bib]      [code]

Wasserstein barycenters can be computed in polynomial time in fixed dimension
Jason M Altschuler, Enric Boix-Adsera, 2021.
[abs][pdf][bib]      [code]

Banach Space Representer Theorems for Neural Networks and Ridge Splines
Rahul Parhi, Robert D. Nowak, 2021.
[abs][pdf][bib]

High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm
Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan, 2021.
[abs][pdf][bib]

From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction
Henning Lange, Steven L. Brunton, J. Nathan Kutz, 2021.
[abs][pdf][bib]      [code]

Residual Energy-Based Models for Text
Anton Bakhtin, Yuntian Deng, Sam Gross, Myle Ott, Marc'Aurelio Ranzato, Arthur Szlam, 2021.
[abs][pdf][bib]

giotto-tda: : A Topological Data Analysis Toolkit for Machine Learning and Data Exploration
Guillaume Tauzin, Umberto Lupo, Lewis Tunstall, Julian Burella Pérez, Matteo Caorsi, Anibal M. Medina-Mardones, Alberto Dassatti, Kathryn Hess, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Risk-Averse Learning by Temporal Difference Methods with Markov Risk Measures
Umit Köse, Andrzej Ruszczyński, 2021.
[abs][pdf][bib]

A Bayesian Contiguous Partitioning Method for Learning Clustered Latent Variables
Zhao Tang Luo, Huiyan Sang, Bani Mallick, 2021.
[abs][pdf][bib]

Multi-class Gaussian Process Classification with Noisy Inputs
Carlos Villacampa-Calvo, Bryan Zaldívar, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, 2021.
[abs][pdf][bib]      [code]

Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation
Melkior Ornik, Ufuk Topcu, 2021.
[abs][pdf][bib]

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, 2021.
[abs][pdf][bib]      [code]

Asynchronous Online Testing of Multiple Hypotheses
Tijana Zrnic, Aaditya Ramdas, Michael I. Jordan, 2021.
[abs][pdf][bib]

Learning interaction kernels in heterogeneous systems of agents from multiple trajectories
Fei Lu, Mauro Maggioni, Sui Tang, 2021.
[abs][pdf][bib]      [code]

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, 2021.
[abs][pdf][bib]      [website]

A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms
Oliver Kroemer, Scott Niekum, George Konidaris, 2021.
[abs][pdf][bib]

Single and Multiple Change-Point Detection with Differential Privacy
Wanrong Zhang, Sara Krehbiel, Rui Tuo, Yajun Mei, Rachel Cummings, 2021.
[abs][pdf][bib]

Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits
Julian Zimmert, Yevgeny Seldin, 2021.
[abs][pdf][bib]

Inference In High-dimensional Single-Index Models Under Symmetric Designs
Hamid Eftekhari, Moulinath Banerjee, Ya'acov Ritov, 2021.
[abs][pdf][bib]      [code]

Finite Time LTI System Identification
Tuhin Sarkar, Alexander Rakhlin, Munther A. Dahleh, 2021.
[abs][pdf][bib]

Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions
Yunwen Lei, Ting Hu, Ke Tang, 2021.
[abs][pdf][bib]

Entangled Kernels - Beyond Separability
Riikka Huusari, Hachem Kadri, 2021.
[abs][pdf][bib]      [code]

A Two-Level Decomposition Framework Exploiting First and Second Order Information for SVM Training Problems
Giulio Galvan, Matteo Lapucci, Chih-Jen Lin, Marco Sciandrone, 2021.
[abs][pdf][bib]

When random initializations help: a study of variational inference for community detection
Purnamrita Sarkar, Y. X. Rachel Wang, Soumendu S. Mukherjee, 2021.
[abs][pdf][bib]

A Fast Globally Linearly Convergent Algorithm for the Computation of Wasserstein Barycenters
Lei Yang, Jia Li, Defeng Sun, Kim-Chuan Toh, 2021.
[abs][pdf][bib]

Aggregated Hold-Out
Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle, 2021.
[abs][pdf][bib]

Ranking and synchronization from pairwise measurements via SVD
Alexandre d'Aspremont, Mihai Cucuringu, Hemant Tyagi, 2021.
[abs][pdf][bib]

A Unified Sample Selection Framework for Output Noise Filtering: An Error-Bound Perspective
Gaoxia Jiang, Wenjian Wang, Yuhua Qian, Jiye Liang, 2021.
[abs][pdf][bib]

Continuous Time Analysis of Momentum Methods
Nikola B. Kovachki, Andrew M. Stuart, 2021.
[abs][pdf][bib]      [supplementary]

Pykg2vec: A Python Library for Knowledge Graph Embedding
Shih-Yuan Yu, Sujit Rokka Chhetri, Arquimedes Canedo, Palash Goyal, Mohammad Abdullah Al Faruque, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-examples
Jagdeep Singh Bhatia, 2021.
[abs][pdf][bib]

On Multi-Armed Bandit Designs for Dose-Finding Trials
Maryam Aziz, Emilie Kaufmann, Marie-Karelle Riviere, 2021.
[abs][pdf][bib]

Homogeneity Structure Learning in Large-scale Panel Data with Heavy-tailed Errors
Di Xiao, Yuan Ke, Runze Li, 2021.
[abs][pdf][bib]

Global and Quadratic Convergence of Newton Hard-Thresholding Pursuit
Shenglong Zhou, Naihua Xiu, Hou-Duo Qi, 2021.
[abs][pdf][bib]

Unfolding-Model-Based Visualization: Theory, Method and Applications
Yunxiao Chen, Zhiliang Ying, Haoran Zhang, 2021.
[abs][pdf][bib]      [code]

Mixing Time of Metropolis-Hastings for Bayesian Community Detection
Bumeng Zhuo, Chao Gao, 2021.
[abs][pdf][bib]

Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm
Defeng Sun, Kim-Chuan Toh, Yancheng Yuan, 2021.
[abs][pdf][bib]

A Unified Framework for Random Forest Prediction Error Estimation
Benjamin Lu, Johanna Hardin, 2021.
[abs][pdf][bib]

Preference-based Online Learning with Dueling Bandits: A Survey
Viktor Bengs, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, Eyke Hüllermeier, 2021.
[abs][pdf][bib]

Consistent estimation of small masses in feature sampling
Fadhel Ayed, Marco Battiston, Federico Camerlenghi, Stefano Favaro, 2021.
[abs][pdf][bib]

The Decoupled Extended Kalman Filter for Dynamic Exponential-Family Factorization Models
Carlos A. Gomez-Uribe, Brian Karrer, 2021.
[abs][pdf][bib]

An Empirical Study of Bayesian Optimization: Acquisition Versus Partition
Erich Merrill, Alan Fern, Xiaoli Fern, Nima Dolatnia, 2021.
[abs][pdf][bib]      [code]

Regulating Greed Over Time in Multi-Armed Bandits
Stefano Tracà, Cynthia Rudin, Weiyu Yan, 2021.
[abs][pdf][bib]      [code]

Domain Generalization by Marginal Transfer Learning
Gilles Blanchard, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, Clayton Scott, 2021.
[abs][pdf][bib]      [code]

On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests
Krishnakumar Balasubramanian, Tong Li, Ming Yuan, 2021.
[abs][pdf][bib]

Full list

© JMLR 2021.