JMLR Volume 22

• On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests

  Krishnakumar Balasubramanian, Tong Li, Ming Yuan (1):1−45, 2021 PDF BibTeX

• Domain Generalization by Marginal Transfer Learning

  Gilles Blanchard, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, Clayton Scott (2):1−55, 2021 codePDF BibTeX

• Regulating Greed Over Time in Multi-Armed Bandits

  Stefano Tracà, Cynthia Rudin, Weiyu Yan (3):1−99, 2021 codePDF BibTeX

• An Empirical Study of Bayesian Optimization: Acquisition Versus Partition

  Erich Merrill, Alan Fern, Xiaoli Fern, Nima Dolatnia (4):1−25, 2021 codePDF BibTeX

• The Decoupled Extended Kalman Filter for Dynamic Exponential-Family Factorization Models

  Carlos A. Gomez-Uribe, Brian Karrer (5):1−25, 2021 PDF BibTeX

• Consistent estimation of small masses in feature sampling

  Fadhel Ayed, Marco Battiston, Federico Camerlenghi, Stefano Favaro (6):1−28, 2021 PDF BibTeX

• Preference-based Online Learning with Dueling Bandits: A Survey

  Viktor Bengs, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, Eyke Hüllermeier (7):1−108, 2021 PDF BibTeX

• A Unified Framework for Random Forest Prediction Error Estimation

  Benjamin Lu, Johanna Hardin (8):1−41, 2021 PDF BibTeX

• Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm

  Defeng Sun, Kim-Chuan Toh, Yancheng Yuan (9):1−32, 2021 PDF BibTeX

• Mixing Time of Metropolis-Hastings for Bayesian Community Detection

  Bumeng Zhuo, Chao Gao (10):1−89, 2021 PDF BibTeX

• Unfolding-Model-Based Visualization: Theory, Method and Applications

  Yunxiao Chen, Zhiliang Ying, Haoran Zhang (11):1−51, 2021 codePDF BibTeX

• Global and Quadratic Convergence of Newton Hard-Thresholding Pursuit

  Shenglong Zhou, Naihua Xiu, Hou-Duo Qi (12):1−45, 2021 PDF BibTeX

• Homogeneity Structure Learning in Large-scale Panel Data with Heavy-tailed Errors

  Di Xiao, Yuan Ke, Runze Li (13):1−42, 2021 PDF BibTeX

• On Multi-Armed Bandit Designs for Dose-Finding Trials

  Maryam Aziz, Emilie Kaufmann, Marie-Karelle Riviere (14):1−38, 2021 PDF BibTeX

• Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-examples

  Jagdeep Singh Bhatia (15):1−30, 2021 PDF BibTeX

• Pykg2vec: A Python Library for Knowledge Graph Embedding

  Shih-Yuan Yu, Sujit Rokka Chhetri, Arquimedes Canedo, Palash Goyal, Mohammad Abdullah Al Faruque (16):1−6, 2021 codePDF BibTeX

• Continuous Time Analysis of Momentum Methods

  Nikola B. Kovachki, Andrew M. Stuart (17):1−40, 2021 supplementaryPDF BibTeX

• A Unified Sample Selection Framework for Output Noise Filtering: An Error-Bound Perspective

  Gaoxia Jiang, Wenjian Wang, Yuhua Qian, Jiye Liang (18):1−66, 2021 PDF BibTeX

• Ranking and synchronization from pairwise measurements via SVD

  Alexandre d'Aspremont, Mihai Cucuringu, Hemant Tyagi (19):1−63, 2021 PDF BibTeX

• Aggregated Hold-Out

  Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle (20):1−55, 2021 PDF BibTeX

• A Fast Globally Linearly Convergent Algorithm for the Computation of Wasserstein Barycenters

  Lei Yang, Jia Li, Defeng Sun, Kim-Chuan Toh (21):1−37, 2021 PDF BibTeX

• When random initializations help: a study of variational inference for community detection

  Purnamrita Sarkar, Y. X. Rachel Wang, Soumendu S. Mukherjee (22):1−46, 2021 PDF BibTeX

• A Two-Level Decomposition Framework Exploiting First and Second Order Information for SVM Training Problems

  Giulio Galvan, Matteo Lapucci, Chih-Jen Lin, Marco Sciandrone (23):1−38, 2021 PDF BibTeX

• Entangled Kernels - Beyond Separability

  Riikka Huusari, Hachem Kadri (24):1−40, 2021 codePDF BibTeX

• Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions

  Yunwen Lei, Ting Hu, Ke Tang (25):1−41, 2021 PDF BibTeX

• Finite Time LTI System Identification

  Tuhin Sarkar, Alexander Rakhlin, Munther A. Dahleh (26):1−61, 2021 PDF BibTeX

• Inference In High-dimensional Single-Index Models Under Symmetric Designs

  Hamid Eftekhari, Moulinath Banerjee, Ya'acov Ritov (27):1−63, 2021 codePDF BibTeX

• Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits

  Julian Zimmert, Yevgeny Seldin (28):1−49, 2021 PDF BibTeX

• Single and Multiple Change-Point Detection with Differential Privacy

  Wanrong Zhang, Sara Krehbiel, Rui Tuo, Yajun Mei, Rachel Cummings (29):1−36, 2021 PDF BibTeX

• A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms

  Oliver Kroemer, Scott Niekum, George Konidaris (30):1−82, 2021 PDF BibTeX

• 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 (31):1−41, 2021 websitePDF BibTeX

• Learning interaction kernels in heterogeneous systems of agents from multiple trajectories

  Fei Lu, Mauro Maggioni, Sui Tang (32):1−67, 2021 codePDF BibTeX

• Asynchronous Online Testing of Multiple Hypotheses

  Tijana Zrnic, Aaditya Ramdas, Michael I. Jordan (33):1−39, 2021 PDF BibTeX

• 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 (34):1−32, 2021 codePDF BibTeX

• Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation

  Melkior Ornik, Ufuk Topcu (35):1−40, 2021 PDF BibTeX

• Multi-class Gaussian Process Classification with Noisy Inputs

  Carlos Villacampa-Calvo, Bryan Zaldívar, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato (36):1−52, 2021 codePDF BibTeX

• A Bayesian Contiguous Partitioning Method for Learning Clustered Latent Variables

  Zhao Tang Luo, Huiyan Sang, Bani Mallick (37):1−52, 2021 PDF BibTeX

• Risk-Averse Learning by Temporal Difference Methods with Markov Risk Measures

  Umit Köse, Andrzej Ruszczyński (38):1−34, 2021 PDF BibTeX

• 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 (39):1−6, 2021 codePDF BibTeX

• Residual Energy-Based Models for Text

  Anton Bakhtin, Yuntian Deng, Sam Gross, Myle Ott, Marc'Aurelio Ranzato, Arthur Szlam (40):1−41, 2021 PDF BibTeX

• From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction

  Henning Lange, Steven L. Brunton, J. Nathan Kutz (41):1−38, 2021 codePDF BibTeX

• High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm

  Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan (42):1−41, 2021 PDF BibTeX

• Banach Space Representer Theorems for Neural Networks and Ridge Splines

  Rahul Parhi, Robert D. Nowak (43):1−40, 2021 PDF BibTeX

• Wasserstein barycenters can be computed in polynomial time in fixed dimension

  Jason M Altschuler, Enric Boix-Adsera (44):1−19, 2021 codePDF BibTeX

• RaSE: Random Subspace Ensemble Classification

  Ye Tian, Yang Feng (45):1−93, 2021 codePDF BibTeX

• Optimal Structured Principal Subspace Estimation: Metric Entropy and Minimax Rates

  Tony Cai, Hongzhe Li, Rong Ma (46):1−45, 2021 PDF BibTeX

• Understanding Recurrent Neural Networks Using Nonequilibrium Response Theory

  Soon Hoe Lim (47):1−48, 2021 PDF BibTeX

• Optimal Feedback Law Recovery by Gradient-Augmented Sparse Polynomial Regression

  Behzad Azmi, Dante Kalise, Karl Kunisch (48):1−32, 2021 PDF BibTeX

• From Low Probability to High Confidence in Stochastic Convex Optimization

  Damek Davis, Dmitriy Drusvyatskiy, Lin Xiao, Junyu Zhang (49):1−38, 2021 PDF BibTeX

• Structure Learning of Undirected Graphical Models for Count Data

  Nguyen Thi Kim Hue, Monica Chiogna (50):1−53, 2021 PDF BibTeX

• Projection-free Decentralized Online Learning for Submodular Maximization over Time-Varying Networks

  Junlong Zhu, Qingtao Wu, Mingchuan Zhang, Ruijuan Zheng, Keqin Li (51):1−42, 2021 PDF BibTeX

• Sparse and Smooth Signal Estimation: Convexification of L0-Formulations

  Alper Atamturk, Andres Gomez, Shaoning Han (52):1−43, 2021 PDF BibTeX

• Subspace Clustering through Sub-Clusters

  Weiwei Li, Jan Hannig, Sayan Mukherjee (53):1−37, 2021 PDF BibTeX

• GemBag: Group Estimation of Multiple Bayesian Graphical Models

  Xinming Yang, Lingrui Gan, Naveen N. Narisetty, Feng Liang (54):1−48, 2021 PDF BibTeX

• Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data

  Minjie Wang, Genevera I. Allen (55):1−73, 2021 PDF BibTeX

• Incorporating Unlabeled Data into Distributionally Robust Learning

  Charlie Frogner, Sebastian Claici, Edward Chien, Justin Solomon (56):1−46, 2021 PDF BibTeX

• Normalizing Flows for Probabilistic Modeling and Inference

  George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan (57):1−64, 2021 PDF BibTeX

• Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing Approach

  Zhe Fei, Yi Li (58):1−32, 2021 codePDF BibTeX

• Predictive Learning on Hidden Tree-Structured Ising Models

  Konstantinos E. Nikolakakis, Dionysios S. Kalogerias, Anand D. Sarwate (59):1−82, 2021 codePDF BibTeX

• A Distributed Method for Fitting Laplacian Regularized Stratified Models

  Jonathan Tuck, Shane Barratt, Stephen Boyd (60):1−37, 2021 codePDF BibTeX

• Stochastic Proximal AUC Maximization

  Yunwen Lei, Yiming Ying (61):1−45, 2021 PDF BibTeX

• How to Gain on Power: Novel Conditional Independence Tests Based on Short Expansion of Conditional Mutual Information

  Mariusz Kubkowski, Jan Mielniczuk, Paweł Teisseyre (62):1−57, 2021 PDF BibTeX

• Geometric structure of graph Laplacian embeddings

  Nicolás García Trillos, Franca Hoffmann, Bamdad Hosseini (63):1−55, 2021 PDF BibTeX

• Sparse Tensor Additive Regression

  Botao Hao, Boxiang Wang, Pengyuan Wang, Jingfei Zhang, Jian Yang, Will Wei Sun (64):1−43, 2021 PDF BibTeX

• Dynamic Tensor Recommender Systems

  Yanqing Zhang, Xuan Bi, Niansheng Tang, Annie Qu (65):1−35, 2021 PDF BibTeX

• Approximate Newton Methods

  Haishan Ye, Luo Luo, Zhihua Zhang (66):1−41, 2021 PDF BibTeX

• A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family

  Trambak Banerjee, Qiang Liu, Gourab Mukherjee, Wengunag Sun (67):1−46, 2021 PDF BibTeX

• Path Length Bounds for Gradient Descent and Flow

  Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas (68):1−63, 2021 blogPDF BibTeX

• Determining the Number of Communities in Degree-corrected Stochastic Block Models

  Shujie Ma, Liangjun Su, Yichong Zhang (69):1−63, 2021 PDF BibTeX

• Testing Conditional Independence via Quantile Regression Based Partial Copulas

  Lasse Petersen, Niels Richard Hansen (70):1−47, 2021 codePDF BibTeX

• Phase Diagram for Two-layer ReLU Neural Networks at Infinite-width Limit

  Tao Luo, Zhi-Qin John Xu, Zheng Ma, Yaoyu Zhang (71):1−47, 2021 codePDF BibTeX

• Prediction against a limited adversary

  Erhan Bayraktar, Ibrahim Ekren, Xin Zhang (72):1−33, 2021 PDF BibTeX

• Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives

  Michael Muehlebach, Michael I. Jordan (73):1−50, 2021 PDF BibTeX

• Kernel Operations on the GPU, with Autodiff, without Memory Overflows

  Benjamin Charlier, Jean Feydy, Joan Alexis Glaunès, François-David Collin, Ghislain Durif (74):1−6, 2021 codePDF BibTeX

• Attention is Turing-Complete

  Jorge Pérez, Pablo Barceló, Javier Marinkovic (75):1−35, 2021 PDF BibTeX

• Analyzing the discrepancy principle for kernelized spectral filter learning algorithms

  Alain Celisse, Martin Wahl (76):1−59, 2021 PDF BibTeX

• ChainerRL: A Deep Reinforcement Learning Library

  Yasuhiro Fujita, Prabhat Nagarajan, Toshiki Kataoka, Takahiro Ishikawa (77):1−14, 2021 codePDF BibTeX

• 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 (78):1−8, 2021 codePDF BibTeX

• Is SGD a Bayesian sampler? Well, almost

  Chris Mingard, Guillermo Valle-Pérez, Joar Skalse, Ard A. Louis (79):1−64, 2021 PDF BibTeX

• Communication-Efficient Distributed Covariance Sketch, with Application to Distributed PCA

  Zengfeng Huang, Xuemin Lin, Wenjie Zhang, Ying Zhang (80):1−38, 2021 PDF BibTeX

• Knowing what You Know: valid and validated confidence sets in multiclass and multilabel prediction

  Maxime Cauchois, Suyash Gupta, John C. Duchi (81):1−42, 2021 PDF BibTeX

• 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 (82):1−6, 2021 PDF BibTeX

• Statistical Query Lower Bounds for Tensor PCA

  Rishabh Dudeja, Daniel Hsu (83):1−51, 2021 PDF BibTeX

• Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference

  Jiyuan Tu, Weidong Liu, Xiaojun Mao, Xi Chen (84):1−67, 2021 PDF BibTeX

• Gradient Methods Never Overfit On Separable Data

  Ohad Shamir (85):1−20, 2021 PDF BibTeX

• Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis

  Andreas Damianou, Neil D. Lawrence, Carl Henrik Ek (86):1−51, 2021 PDF BibTeX

• On Solving Probabilistic Linear Diophantine Equations

  Patrick Kreitzberg, Oliver Serang (87):1−24, 2021 codePDF BibTeX

• Edge Sampling Using Local Network Information

  Can M. Le (88):1−29, 2021 PDF BibTeX

• Bayesian Text Classification and Summarization via A Class-Specified Topic Model

  Feifei Wang, Junni L. Zhang, Yichao Li, Ke Deng, Jun S. Liu (89):1−48, 2021 PDF BibTeX

• Risk Bounds for Unsupervised Cross-Domain Mapping with IPMs

  Tomer Galanti, Sagie Benaim, Lior Wolf (90):1−42, 2021 codePDF BibTeX

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

  Tingting Zhao, Alexandre Bouchard-Côté (91):1−41, 2021 codePDF BibTeX

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

  Anastasis Kratsios, Cody Hyndman (92):1−51, 2021 codePDF BibTeX

• Flexible Signal Denoising via Flexible Empirical Bayes Shrinkage

  Zhengrong Xing, Peter Carbonetto, Matthew Stephens (93):1−28, 2021 codePDF BibTeX

• Consistent Semi-Supervised Graph Regularization for High Dimensional Data

  Xiaoyi Mai, Romain Couillet (94):1−48, 2021 PDF BibTeX

• Histogram Transform Ensembles for Large-scale Regression

  Hanyuan Hang, Zhouchen Lin, Xiaoyu Liu, Hongwei Wen (95):1−87, 2021 PDF BibTeX

• Guided Visual Exploration of Relations in Data Sets

  Kai Puolamäki, Emilia Oikarinen, Andreas Henelius (96):1−32, 2021 codePDF BibTeX

• Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach

  Alberto Maria Metelli, Matteo Pirotta, Daniele Calandriello, Marcello Restelli (97):1−83, 2021 PDF BibTeX

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

  Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan (98):1−76, 2021 PDF BibTeX

• Adaptive estimation of nonparametric functionals

  Lin Liu, Rajarshi Mukherjee, James M. Robins, Eric Tchetgen Tchetgen (99):1−66, 2021 PDF BibTeX

• 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 (100):1−5, 2021 codePDF BibTeX

• LocalGAN: Modeling Local Distributions for Adversarial Response Generation

  Baoxun Wang, Zhen Xu, Huan Zhang, Kexin Qiu, Deyuan Zhang, Chengjie Sun (101):1−29, 2021 codePDF BibTeX

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

  Gunwoong Park, Sang Jun Moon, Sion Park, Jong-June Jeon (102):1−41, 2021 PDF BibTeX

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

  Guodong Zhang, Xuchan Bao, Laurent Lessard, Roger Grosse (103):1−39, 2021 codePDF BibTeX

• Explaining Explanations: Axiomatic Feature Interactions for Deep Networks

  Joseph D. Janizek, Pascal Sturmfels, Su-In Lee (104):1−54, 2021 codePDF BibTeX

• Pathwise Conditioning of Gaussian Processes

  James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth (105):1−47, 2021 codePDF BibTeX

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

  Gerard Ben Arous, Reza Gheissari, Aukosh Jagannath (106):1−51, 2021 PDF BibTeX

• 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 (107):1−48, 2021 codePDF BibTeX

• Towards a Unified Analysis of Random Fourier Features

  Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic (108):1−51, 2021 PDF BibTeX

• 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 (109):1−7, 2021 codePDF BibTeX

• 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 (110):1−8, 2021 codePDF BibTeX

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

  Steven Siwei Ye, Oscar Hernan Madrid Padilla (111):1−38, 2021 codePDF BibTeX

• L-SVRG and L-Katyusha with Arbitrary Sampling

  Xun Qian, Zheng Qu, Peter Richtárik (112):1−47, 2021 PDF BibTeX

• A Lyapunov Analysis of Accelerated Methods in Optimization

  Ashia C. Wilson, Ben Recht, Michael I. Jordan (113):1−34, 2021 PDF BibTeX

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

  Ali Ramezani-Kebrya, Fartash Faghri, Ilya Markov, Vitalii Aksenov, Dan Alistarh, Daniel M. Roy (114):1−43, 2021 PDF BibTeX

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

  Michael R. Metel, Akiko Takeda (115):1−36, 2021 PDF BibTeX

• An Importance Weighted Feature Selection Stability Measure

  Victor Hamer, Pierre Dupont (116):1−57, 2021 PDF BibTeX

• Strong Consistency, Graph Laplacians, and the Stochastic Block Model

  Shaofeng Deng, Shuyang Ling, Thomas Strohmer (117):1−44, 2021 PDF BibTeX

• A General Framework for Adversarial Label Learning

  Chidubem Arachie, Bert Huang (118):1−33, 2021 codePDF BibTeX

• Some Theoretical Insights into Wasserstein GANs

  Gérard Biau, Maxime Sangnier, Ugo Tanielian (119):1−45, 2021 PDF BibTeX

• Empirical Bayes Matrix Factorization

  Wei Wang, Matthew Stephens (120):1−40, 2021 codePDF BibTeX

• Langevin Dynamics for Adaptive Inverse Reinforcement Learning of Stochastic Gradient Algorithms

  Vikram Krishnamurthy, George Yin (121):1−49, 2021 PDF BibTeX

• Sparse Convex Optimization via Adaptively Regularized Hard Thresholding

  Kyriakos Axiotis, Maxim Sviridenko (122):1−47, 2021 PDF BibTeX

• Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness

  George Wynne, François-Xavier Briol, Mark Girolami (123):1−40, 2021 PDF BibTeX

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

  Jingyi Jessica Li, Yiling Elaine Chen, Xin Tong (124):1−54, 2021 codePDF BibTeX

• Bandit Convex Optimization in Non-stationary Environments

  Peng Zhao, Guanghui Wang, Lijun Zhang, Zhi-Hua Zhou (125):1−45, 2021 PDF BibTeX

• Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints

  Molei Liu, Yin Xia, Kelly Cho, Tianxi Cai (126):1−26, 2021 PDF BibTeX

• LassoNet: A Neural Network with Feature Sparsity

  Ismael Lemhadri, Feng Ruan, Louis Abraham, Robert Tibshirani (127):1−29, 2021 codePDF BibTeX

• Optimal Bounds between f-Divergences and Integral Probability Metrics

  Rohit Agrawal, Thibaut Horel (128):1−59, 2021 PDF BibTeX

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

  Niladri S. Chatterji, Philip M. Long (129):1−30, 2021 PDF BibTeX

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

  Steve Hanneke (130):1−116, 2021 PDF BibTeX

• MushroomRL: Simplifying Reinforcement Learning Research

  Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters (131):1−5, 2021 codePDF BibTeX

• Locally Differentially-Private Randomized Response for Discrete Distribution Learning

  Adriano Pastore, Michael Gastpar (132):1−56, 2021 PDF BibTeX

• A Contextual Bandit Bake-off

  Alberto Bietti, Alekh Agarwal, John Langford (133):1−49, 2021 codePDF BibTeX

• An Inertial Newton Algorithm for Deep Learning

  Camille Castera, Jérôme Bolte, Cédric Févotte, Edouard Pauwels (134):1−31, 2021 codePDF BibTeX

• Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives

  Antoine Dedieu, Hussein Hazimeh, Rahul Mazumder (135):1−47, 2021 codePDF BibTeX

• Implicit Langevin Algorithms for Sampling From Log-concave Densities

  Liam Hodgkinson, Robert Salomone, Fred Roosta (136):1−30, 2021 PDF BibTeX

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

  Tong Wang, Qihang Lin (137):1−38, 2021 codePDF BibTeX

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

  Yunzhang Zhu, Renxiong Liu (138):1−62, 2021 PDF BibTeX

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

  Jianqing Fan, Bai Jiang, Qiang Sun (139):1−35, 2021 PDF BibTeX

• Generalization Properties of hyper-RKHS and its Applications

  Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A.K. Suykens (140):1−38, 2021 PDF BibTeX

• Pseudo-Marginal Hamiltonian Monte Carlo

  Johan Alenlöv, Arnoud Doucet, Fredrik Lindsten (141):1−45, 2021 PDF BibTeX

• Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace

  Jesús Arroyo, Avanti Athreya, Joshua Cape, Guodong Chen, Carey E. Priebe, Joshua T. Vogelstein (142):1−49, 2021 codePDF BibTeX

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

  Henning Petzka, Cristian Sminchisescu (143):1−34, 2021 PDF BibTeX

• Individual Fairness in Hindsight

  Swati Gupta, Vijay Kamble (144):1−35, 2021 PDF BibTeX

• On efficient multilevel Clustering via Wasserstein distances

  Viet Huynh, Nhat Ho, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, Dinh Phung (145):1−43, 2021 PDF BibTeX

• Nonparametric Modeling of Higher-Order Interactions via Hypergraphons

  Krishnakumar Balasubramanian (146):1−35, 2021 PDF BibTeX

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

  Meiling Hao, Lianqiang Qu, Dehan Kong, Liuquan Sun, Hongtu Zhu (147):1−39, 2021 PDF BibTeX

• Statistical guarantees for local graph clustering

  Wooseok Ha, Kimon Fountoulakis, Michael W. Mahoney (148):1−54, 2021 PDF BibTeX

• Hyperparameter Optimization via Sequential Uniform Designs

  Zebin Yang, Aijun Zhang (149):1−47, 2021 codePDF BibTeX

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

  Tian Tong, Cong Ma, Yuejie Chi (150):1−63, 2021 codePDF BibTeX

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

  László Györfi, Roi Weiss (151):1−25, 2021 PDF BibTeX

• Hardness of Identity Testing for Restricted Boltzmann Machines and Potts models

  Antonio Blanca, Zongchen Chen, Daniel Štefankovič, Eric Vigoda (152):1−56, 2021 PDF BibTeX

• Factorization Machines with Regularization for Sparse Feature Interactions

  Kyohei Atarashi, Satoshi Oyama, Masahito Kurihara (153):1−50, 2021 PDF BibTeX

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

  Yikun Zhang, Yen-Chi Chen (154):1−92, 2021 codePDF BibTeX

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

  Licong Lin, Edgar Dobriban (155):1−82, 2021 PDF BibTeX

• A Greedy Algorithm for Quantizing Neural Networks

  Eric Lybrand, Rayan Saab (156):1−38, 2021 codePDF BibTeX

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

  Takuo Matsubara, Chris J. Oates, François-Xavier Briol (157):1−57, 2021 PDF BibTeX

• Information criteria for non-normalized models

  Takeru Matsuda, Masatoshi Uehara, Aapo Hyvarinen (158):1−33, 2021 PDF BibTeX

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

  Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett (159):1−48, 2021 PDF BibTeX

• 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 (160):1−78, 2021 codePDF BibTeX

• MetaGrad: Adaptation using Multiple Learning Rates in Online Learning

  Tim van Erven, Wouter M. Koolen, Dirk van der Hoeven (161):1−61, 2021 codePDF BibTeX

• Counterfactual Mean Embeddings

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