JMLR Volume 22

On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests: Krishnakumar Balasubramanian, Tong Li, Ming Yuan; (1):1−45, 2021.
[abs][pdf][bib]

Domain Generalization by Marginal Transfer Learning: Gilles Blanchard, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, Clayton Scott; (2):1−55, 2021.
[abs][pdf][bib] [code]

Regulating Greed Over Time in Multi-Armed Bandits: Stefano Tracà, Cynthia Rudin, Weiyu Yan; (3):1−99, 2021.
[abs][pdf][bib] [code]

An Empirical Study of Bayesian Optimization: Acquisition Versus Partition: Erich Merrill, Alan Fern, Xiaoli Fern, Nima Dolatnia; (4):1−25, 2021.
[abs][pdf][bib] [code]

The Decoupled Extended Kalman Filter for Dynamic Exponential-Family Factorization Models: Carlos A. Gomez-Uribe, Brian Karrer; (5):1−25, 2021.
[abs][pdf][bib]

Consistent estimation of small masses in feature sampling: Fadhel Ayed, Marco Battiston, Federico Camerlenghi, Stefano Favaro; (6):1−28, 2021.
[abs][pdf][bib]

Preference-based Online Learning with Dueling Bandits: A Survey: Viktor Bengs, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, Eyke Hüllermeier; (7):1−108, 2021.
[abs][pdf][bib]

A Unified Framework for Random Forest Prediction Error Estimation: Benjamin Lu, Johanna Hardin; (8):1−41, 2021.
[abs][pdf][bib]

Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm: Defeng Sun, Kim-Chuan Toh, Yancheng Yuan; (9):1−32, 2021.
[abs][pdf][bib]

Mixing Time of Metropolis-Hastings for Bayesian Community Detection: Bumeng Zhuo, Chao Gao; (10):1−89, 2021.
[abs][pdf][bib]

Unfolding-Model-Based Visualization: Theory, Method and Applications: Yunxiao Chen, Zhiliang Ying, Haoran Zhang; (11):1−51, 2021.
[abs][pdf][bib] [code]

Global and Quadratic Convergence of Newton Hard-Thresholding Pursuit: Shenglong Zhou, Naihua Xiu, Hou-Duo Qi; (12):1−45, 2021.
[abs][pdf][bib]

Homogeneity Structure Learning in Large-scale Panel Data with Heavy-tailed Errors: Di Xiao, Yuan Ke, Runze Li; (13):1−42, 2021.
[abs][pdf][bib]

On Multi-Armed Bandit Designs for Dose-Finding Trials: Maryam Aziz, Emilie Kaufmann, Marie-Karelle Riviere; (14):1−38, 2021.
[abs][pdf][bib]

Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-examples: Jagdeep Singh Bhatia; (15):1−30, 2021.
[abs][pdf][bib]

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. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Continuous Time Analysis of Momentum Methods: Nikola B. Kovachki, Andrew M. Stuart; (17):1−40, 2021.
[abs][pdf][bib] [supplementary]

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.
[abs][pdf][bib]

Ranking and synchronization from pairwise measurements via SVD: Alexandre d'Aspremont, Mihai Cucuringu, Hemant Tyagi; (19):1−63, 2021.
[abs][pdf][bib]

Aggregated Hold-Out: Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle; (20):1−55, 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; (21):1−37, 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; (22):1−46, 2021.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Entangled Kernels - Beyond Separability: Riikka Huusari, Hachem Kadri; (24):1−40, 2021.
[abs][pdf][bib] [code]

Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions: Yunwen Lei, Ting Hu, Ke Tang; (25):1−41, 2021.
[abs][pdf][bib]

Finite Time LTI System Identification: Tuhin Sarkar, Alexander Rakhlin, Munther A. Dahleh; (26):1−61, 2021.
[abs][pdf][bib]

Inference In High-dimensional Single-Index Models Under Symmetric Designs: Hamid Eftekhari, Moulinath Banerjee, Ya'acov Ritov; (27):1−63, 2021.
[abs][pdf][bib] [code]

Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits: Julian Zimmert, Yevgeny Seldin; (28):1−49, 2021.
[abs][pdf][bib]

Single and Multiple Change-Point Detection with Differential Privacy: Wanrong Zhang, Sara Krehbiel, Rui Tuo, Yajun Mei, Rachel Cummings; (29):1−36, 2021.
[abs][pdf][bib]

A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms: Oliver Kroemer, Scott Niekum, George Konidaris; (30):1−82, 2021.
[abs][pdf][bib]

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.
[abs][pdf][bib] [website]

Learning interaction kernels in heterogeneous systems of agents from multiple trajectories: Fei Lu, Mauro Maggioni, Sui Tang; (32):1−67, 2021.
[abs][pdf][bib] [code]

Asynchronous Online Testing of Multiple Hypotheses: Tijana Zrnic, Aaditya Ramdas, Michael I. Jordan; (33):1−39, 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; (34):1−32, 2021.
[abs][pdf][bib] [code]

Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation: Melkior Ornik, Ufuk Topcu; (35):1−40, 2021.
[abs][pdf][bib]

Multi-class Gaussian Process Classification with Noisy Inputs: Carlos Villacampa-Calvo, Bryan Zaldívar, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato; (36):1−52, 2021.
[abs][pdf][bib] [code]

A Bayesian Contiguous Partitioning Method for Learning Clustered Latent Variables: Zhao Tang Luo, Huiyan Sang, Bani Mallick; (37):1−52, 2021.
[abs][pdf][bib]

Risk-Averse Learning by Temporal Difference Methods with Markov Risk Measures: Umit Köse, Andrzej Ruszczyński; (38):1−34, 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; (39):1−6, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Residual Energy-Based Models for Text: Anton Bakhtin, Yuntian Deng, Sam Gross, Myle Ott, Marc'Aurelio Ranzato, Arthur Szlam; (40):1−41, 2021.
[abs][pdf][bib]

From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction: Henning Lange, Steven L. Brunton, J. Nathan Kutz; (41):1−38, 2021.
[abs][pdf][bib] [code]

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.
[abs][pdf][bib]

Banach Space Representer Theorems for Neural Networks and Ridge Splines: Rahul Parhi, Robert D. Nowak; (43):1−40, 2021.
[abs][pdf][bib]

Wasserstein barycenters can be computed in polynomial time in fixed dimension: Jason M Altschuler, Enric Boix-Adsera; (44):1−19, 2021.
[abs][pdf][bib] [code]

RaSE: Random Subspace Ensemble Classification: Ye Tian, Yang Feng; (45):1−93, 2021.
[abs][pdf][bib] [code]

Optimal Structured Principal Subspace Estimation: Metric Entropy and Minimax Rates: Tony Cai, Hongzhe Li, Rong Ma; (46):1−45, 2021.
[abs][pdf][bib]

Understanding Recurrent Neural Networks Using Nonequilibrium Response Theory: Soon Hoe Lim; (47):1−48, 2021.
[abs][pdf][bib]

Optimal Feedback Law Recovery by Gradient-Augmented Sparse Polynomial Regression: Behzad Azmi, Dante Kalise, Karl Kunisch; (48):1−32, 2021.
[abs][pdf][bib]

From Low Probability to High Confidence in Stochastic Convex Optimization: Damek Davis, Dmitriy Drusvyatskiy, Lin Xiao, Junyu Zhang; (49):1−38, 2021.
[abs][pdf][bib]

Structure Learning of Undirected Graphical Models for Count Data: Nguyen Thi Kim Hue, Monica Chiogna; (50):1−53, 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; (51):1−42, 2021.
[abs][pdf][bib]

Sparse and Smooth Signal Estimation: Convexification of L0-Formulations: Alper Atamturk, Andres Gomez, Shaoning Han; (52):1−43, 2021.
[abs][pdf][bib]

Subspace Clustering through Sub-Clusters: Weiwei Li, Jan Hannig, Sayan Mukherjee; (53):1−37, 2021.
[abs][pdf][bib]

GemBag: Group Estimation of Multiple Bayesian Graphical Models: Xinming Yang, Lingrui Gan, Naveen N. Narisetty, Feng Liang; (54):1−48, 2021.
[abs][pdf][bib]

Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data: Minjie Wang, Genevera I. Allen; (55):1−73, 2021.
[abs][pdf][bib]

Incorporating Unlabeled Data into Distributionally Robust Learning: Charlie Frogner, Sebastian Claici, Edward Chien, Justin Solomon; (56):1−46, 2021.
[abs][pdf][bib]

Normalizing Flows for Probabilistic Modeling and Inference: George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan; (57):1−64, 2021.
[abs][pdf][bib]

Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing Approach: Zhe Fei, Yi Li; (58):1−32, 2021.
[abs][pdf][bib] [code]

Predictive Learning on Hidden Tree-Structured Ising Models: Konstantinos E. Nikolakakis, Dionysios S. Kalogerias, Anand D. Sarwate; (59):1−82, 2021.
[abs][pdf][bib] [code]

A Distributed Method for Fitting Laplacian Regularized Stratified Models: Jonathan Tuck, Shane Barratt, Stephen Boyd; (60):1−37, 2021.
[abs][pdf][bib] [code]

Stochastic Proximal AUC Maximization: Yunwen Lei, Yiming Ying; (61):1−45, 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; (62):1−57, 2021.
[abs][pdf][bib]

Geometric structure of graph Laplacian embeddings: Nicolás García Trillos, Franca Hoffmann, Bamdad Hosseini; (63):1−55, 2021.
[abs][pdf][bib]

Sparse Tensor Additive Regression: Botao Hao, Boxiang Wang, Pengyuan Wang, Jingfei Zhang, Jian Yang, Will Wei Sun; (64):1−43, 2021.
[abs][pdf][bib]

Dynamic Tensor Recommender Systems: Yanqing Zhang, Xuan Bi, Niansheng Tang, Annie Qu; (65):1−35, 2021.
[abs][pdf][bib]

Approximate Newton Methods: Haishan Ye, Luo Luo, Zhihua Zhang; (66):1−41, 2021.
[abs][pdf][bib]

A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family: Trambak Banerjee, Qiang Liu, Gourab Mukherjee, Wengunag Sun; (67):1−46, 2021.
[abs][pdf][bib]

Path Length Bounds for Gradient Descent and Flow: Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas; (68):1−63, 2021.
[abs][pdf][bib] [blog]

Determining the Number of Communities in Degree-corrected Stochastic Block Models: Shujie Ma, Liangjun Su, Yichong Zhang; (69):1−63, 2021.
[abs][pdf][bib]

Testing Conditional Independence via Quantile Regression Based Partial Copulas: Lasse Petersen, Niels Richard Hansen; (70):1−47, 2021.
[abs][pdf][bib] [code]

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.
[abs][pdf][bib] [code]

Prediction against a limited adversary: Erhan Bayraktar, Ibrahim Ekren, Xin Zhang; (72):1−33, 2021.
[abs][pdf][bib]

Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives: Michael Muehlebach, Michael I. Jordan; (73):1−50, 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; (74):1−6, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Attention is Turing-Complete: Jorge Pérez, Pablo Barceló, Javier Marinkovic; (75):1−35, 2021.
[abs][pdf][bib]

Analyzing the discrepancy principle for kernelized spectral filter learning algorithms: Alain Celisse, Martin Wahl; (76):1−59, 2021.
[abs][pdf][bib]

ChainerRL: A Deep Reinforcement Learning Library: Yasuhiro Fujita, Prabhat Nagarajan, Toshiki Kataoka, Takahiro Ishikawa; (77):1−14, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

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. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Is SGD a Bayesian sampler? Well, almost: Chris Mingard, Guillermo Valle-Pérez, Joar Skalse, Ard A. Louis; (79):1−64, 2021.
[abs][pdf][bib]

Communication-Efficient Distributed Covariance Sketch, with Application to Distributed PCA: Zengfeng Huang, Xuemin Lin, Wenjie Zhang, Ying Zhang; (80):1−38, 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; (81):1−42, 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; (82):1−6, 2021.
[abs][pdf][bib]

Statistical Query Lower Bounds for Tensor PCA: Rishabh Dudeja, Daniel Hsu; (83):1−51, 2021.
[abs][pdf][bib]

Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference: Jiyuan Tu, Weidong Liu, Xiaojun Mao, Xi Chen; (84):1−67, 2021.
[abs][pdf][bib]

Gradient Methods Never Overfit On Separable Data: Ohad Shamir; (85):1−20, 2021.
[abs][pdf][bib]

Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis: Andreas Damianou, Neil D. Lawrence, Carl Henrik Ek; (86):1−51, 2021.
[abs][pdf][bib]

On Solving Probabilistic Linear Diophantine Equations: Patrick Kreitzberg, Oliver Serang; (87):1−24, 2021.
[abs][pdf][bib] [code]

Edge Sampling Using Local Network Information: Can M. Le; (88):1−29, 2021.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Risk Bounds for Unsupervised Cross-Domain Mapping with IPMs: Tomer Galanti, Sagie Benaim, Lior Wolf; (90):1−42, 2021.
[abs][pdf][bib] [code]

Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler: Tingting Zhao, Alexandre Bouchard-Côté; (91):1−41, 2021.
[abs][pdf][bib] [code]

NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation: Anastasis Kratsios, Cody Hyndman; (92):1−51, 2021.
[abs][pdf][bib] [code]

Flexible Signal Denoising via Flexible Empirical Bayes Shrinkage: Zhengrong Xing, Peter Carbonetto, Matthew Stephens; (93):1−28, 2021.
[abs][pdf][bib] [code]

Consistent Semi-Supervised Graph Regularization for High Dimensional Data: Xiaoyi Mai, Romain Couillet; (94):1−48, 2021.
[abs][pdf][bib]

Histogram Transform Ensembles for Large-scale Regression: Hanyuan Hang, Zhouchen Lin, Xiaoyu Liu, Hongwei Wen; (95):1−87, 2021.
[abs][pdf][bib]

Guided Visual Exploration of Relations in Data Sets: Kai Puolamäki, Emilia Oikarinen, Andreas Henelius; (96):1−32, 2021.
[abs][pdf][bib] [code]

Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach: Alberto Maria Metelli, Matteo Pirotta, Daniele Calandriello, Marcello Restelli; (97):1−83, 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; (98):1−76, 2021.
[abs][pdf][bib]

Adaptive estimation of nonparametric functionals: Lin Liu, Rajarshi Mukherjee, James M. Robins, Eric Tchetgen Tchetgen; (99):1−66, 2021.
[abs][pdf][bib]

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. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

LocalGAN: Modeling Local Distributions for Adversarial Response Generation: Baoxun Wang, Zhen Xu, Huan Zhang, Kexin Qiu, Deyuan Zhang, Chengjie Sun; (101):1−29, 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; (102):1−41, 2021.
[abs][pdf][bib]

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.
[abs][pdf][bib] [code]

Explaining Explanations: Axiomatic Feature Interactions for Deep Networks: Joseph D. Janizek, Pascal Sturmfels, Su-In Lee; (104):1−54, 2021.
[abs][pdf][bib] [code]

Pathwise Conditioning of Gaussian Processes: James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth; (105):1−47, 2021.
[abs][pdf][bib] [code]

Online stochastic gradient descent on non-convex losses from high-dimensional inference: Gerard Ben Arous, Reza Gheissari, Aukosh Jagannath; (106):1−51, 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; (107):1−48, 2021.
[abs][pdf][bib] [code]

Towards a Unified Analysis of Random Fourier Features: Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic; (108):1−51, 2021.
[abs][pdf][bib]

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. (Machine Learning Open Source Software Paper)
[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; (110):1−8, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Non-parametric Quantile Regression via the K-NN Fused Lasso: Steven Siwei Ye, Oscar Hernan Madrid Padilla; (111):1−38, 2021.
[abs][pdf][bib] [code]

L-SVRG and L-Katyusha with Arbitrary Sampling: Xun Qian, Zheng Qu, Peter Richtárik; (112):1−47, 2021.
[abs][pdf][bib]

A Lyapunov Analysis of Accelerated Methods in Optimization: Ashia C. Wilson, Ben Recht, Michael I. Jordan; (113):1−34, 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; (114):1−43, 2021.
[abs][pdf][bib]

Stochastic Proximal Methods for Non-Smooth Non-Convex Constrained Sparse Optimization: Michael R. Metel, Akiko Takeda; (115):1−36, 2021.
[abs][pdf][bib]

An Importance Weighted Feature Selection Stability Measure: Victor Hamer, Pierre Dupont; (116):1−57, 2021.
[abs][pdf][bib]

Strong Consistency, Graph Laplacians, and the Stochastic Block Model: Shaofeng Deng, Shuyang Ling, Thomas Strohmer; (117):1−44, 2021.
[abs][pdf][bib]

A General Framework for Adversarial Label Learning: Chidubem Arachie, Bert Huang; (118):1−33, 2021.
[abs][pdf][bib] [code]

Some Theoretical Insights into Wasserstein GANs: Gérard Biau, Maxime Sangnier, Ugo Tanielian; (119):1−45, 2021.
[abs][pdf][bib]

Empirical Bayes Matrix Factorization: Wei Wang, Matthew Stephens; (120):1−40, 2021.
[abs][pdf][bib] [code]

Langevin Dynamics for Adaptive Inverse Reinforcement Learning of Stochastic Gradient Algorithms: Vikram Krishnamurthy, George Yin; (121):1−49, 2021.
[abs][pdf][bib]

Sparse Convex Optimization via Adaptively Regularized Hard Thresholding: Kyriakos Axiotis, Maxim Sviridenko; (122):1−47, 2021.
[abs][pdf][bib]

Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness: George Wynne, François-Xavier Briol, Mark Girolami; (123):1−40, 2021.
[abs][pdf][bib]

A flexible model-free prediction-based framework for feature ranking: Jingyi Jessica Li, Yiling Elaine Chen, Xin Tong; (124):1−54, 2021.
[abs][pdf][bib] [code]

Bandit Convex Optimization in Non-stationary Environments: Peng Zhao, Guanghui Wang, Lijun Zhang, Zhi-Hua Zhou; (125):1−45, 2021.
[abs][pdf][bib]

Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints: Molei Liu, Yin Xia, Kelly Cho, Tianxi Cai; (126):1−26, 2021.
[abs][pdf][bib]

LassoNet: A Neural Network with Feature Sparsity: Ismael Lemhadri, Feng Ruan, Louis Abraham, Robert Tibshirani; (127):1−29, 2021.
[abs][pdf][bib] [code]

Optimal Bounds between f-Divergences and Integral Probability Metrics: Rohit Agrawal, Thibaut Horel; (128):1−59, 2021.
[abs][pdf][bib]

Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime: Niladri S. Chatterji, Philip M. Long; (129):1−30, 2021.
[abs][pdf][bib]

Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes: Steve Hanneke; (130):1−116, 2021.
[abs][pdf][bib]

MushroomRL: Simplifying Reinforcement Learning Research: Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters; (131):1−5, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Locally Differentially-Private Randomized Response for Discrete Distribution Learning: Adriano Pastore, Michael Gastpar; (132):1−56, 2021.
[abs][pdf][bib]

A Contextual Bandit Bake-off: Alberto Bietti, Alekh Agarwal, John Langford; (133):1−49, 2021.
[abs][pdf][bib] [code]

An Inertial Newton Algorithm for Deep Learning: Camille Castera, Jérôme Bolte, Cédric Févotte, Edouard Pauwels; (134):1−31, 2021.
[abs][pdf][bib] [code]

Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives: Antoine Dedieu, Hussein Hazimeh, Rahul Mazumder; (135):1−47, 2021.
[abs][pdf][bib] [code]

Implicit Langevin Algorithms for Sampling From Log-concave Densities: Liam Hodgkinson, Robert Salomone, Fred Roosta; (136):1−30, 2021.
[abs][pdf][bib]

Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model: Tong Wang, Qihang Lin; (137):1−38, 2021.
[abs][pdf][bib] [code]

An algorithmic view of L2 regularization and some path-following algorithms: Yunzhang Zhu, Renxiong Liu; (138):1−62, 2021.
[abs][pdf][bib]

Hoeffding's Inequality for General Markov Chains and Its Applications to Statistical Learning: Jianqing Fan, Bai Jiang, Qiang Sun; (139):1−35, 2021.
[abs][pdf][bib]

Generalization Properties of hyper-RKHS and its Applications: Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A.K. Suykens; (140):1−38, 2021.
[abs][pdf][bib]

Pseudo-Marginal Hamiltonian Monte Carlo: Johan Alenlöv, Arnoud Doucet, Fredrik Lindsten; (141):1−45, 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; (142):1−49, 2021.
[abs][pdf][bib] [code]

Non-attracting Regions of Local Minima in Deep and Wide Neural Networks: Henning Petzka, Cristian Sminchisescu; (143):1−34, 2021.
[abs][pdf][bib]

Individual Fairness in Hindsight: Swati Gupta, Vijay Kamble; (144):1−35, 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; (145):1−43, 2021.
[abs][pdf][bib]

Nonparametric Modeling of Higher-Order Interactions via Hypergraphons: Krishnakumar Balasubramanian; (146):1−35, 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; (147):1−39, 2021.
[abs][pdf][bib]

Statistical guarantees for local graph clustering: Wooseok Ha, Kimon Fountoulakis, Michael W. Mahoney; (148):1−54, 2021.
[abs][pdf][bib]

Hyperparameter Optimization via Sequential Uniform Designs: Zebin Yang, Aijun Zhang; (149):1−47, 2021.
[abs][pdf][bib] [code]

Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent: Tian Tong, Cong Ma, Yuejie Chi; (150):1−63, 2021.
[abs][pdf][bib] [code]

Universal consistency and rates of convergence of multiclass prototype algorithms in metric spaces: László Györfi, Roi Weiss; (151):1−25, 2021.
[abs][pdf][bib]

Hardness of Identity Testing for Restricted Boltzmann Machines and Potts models: Antonio Blanca, Zongchen Chen, Daniel Štefankovič, Eric Vigoda; (152):1−56, 2021.
[abs][pdf][bib]

Factorization Machines with Regularization for Sparse Feature Interactions: Kyohei Atarashi, Satoshi Oyama, Masahito Kurihara; (153):1−50, 2021.
[abs][pdf][bib]

Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data: Yikun Zhang, Yen-Chi Chen; (154):1−92, 2021.
[abs][pdf][bib] [code]

What Causes the Test Error? Going Beyond Bias-Variance via ANOVA: Licong Lin, Edgar Dobriban; (155):1−82, 2021.
[abs][pdf][bib]

A Greedy Algorithm for Quantizing Neural Networks: Eric Lybrand, Rayan Saab; (156):1−38, 2021.
[abs][pdf][bib] [code]

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.
[abs][pdf][bib]

Information criteria for non-normalized models: Takeru Matsuda, Masatoshi Uehara, Aapo Hyvarinen; (158):1−33, 2021.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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.
[abs][pdf][bib] [code]

MetaGrad: Adaptation using Multiple Learning Rates in Online Learning: Tim van Erven, Wouter M. Koolen, Dirk van der Hoeven; (161):1−61, 2021.
[abs][pdf][bib] [code]

Counterfactual Mean Embeddings: Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, Sanparith Marukatat; (162):1−71, 2021.
[abs][pdf][bib] [code]

PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review: Ivan Stelmakh, Nihar Shah, Aarti Singh; (163):1−66, 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; (164):1−20, 2021.
[abs][pdf][bib]

Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning: Charles H. Martin, Michael W. Mahoney; (165):1−73, 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; (166):1−6, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Estimation and Optimization of Composite Outcomes: Daniel J. Luckett, Eric B. Laber, Siyeon Kim, Michael R. Kosorok; (167):1−40, 2021.
[abs][pdf][bib]

Asymptotic Normality, Concentration, and Coverage of Generalized Posteriors: Jeffrey W. Miller; (168):1−53, 2021.
[abs][pdf][bib]

First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems: Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang; (169):1−34, 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; (170):1−47, 2021.
[abs][pdf][bib]

Optimal Rates of Distributed Regression with Imperfect Kernels: Hongwei Sun, Qiang Wu; (171):1−34, 2021.
[abs][pdf][bib]

Unlinked Monotone Regression: Fadoua Balabdaoui, Charles R. Doss, Cécile Durot; (172):1−60, 2021.
[abs][pdf][bib]

Replica Exchange for Non-Convex Optimization: Jing Dong, Xin T. Tong; (173):1−59, 2021.
[abs][pdf][bib]

Achieving Fairness in the Stochastic Multi-Armed Bandit Problem: Vishakha Patil, Ganesh Ghalme, Vineet Nair, Y. Narahari; (174):1−31, 2021.
[abs][pdf][bib]

Doubly infinite residual neural networks: a diffusion process approach: Stefano Peluchetti, Stefano Favaro; (175):1−48, 2021.
[abs][pdf][bib]

Locally Private k-Means Clustering: Uri Stemmer; (176):1−30, 2021.
[abs][pdf][bib]

Prediction Under Latent Factor Regression: Adaptive PCR, Interpolating Predictors and Beyond: Xin Bing, Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp; (177):1−50, 2021.
[abs][pdf][bib]

Conditional independences and causal relations implied by sets of equations: Tineke Blom, Mirthe M. van Diepen, Joris M. Mooij; (178):1−62, 2021.
[abs][pdf][bib]

A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration: Yuetian Luo, Garvesh Raskutti, Ming Yuan, Anru R. Zhang; (179):1−48, 2021.
[abs][pdf][bib]

Improved Shrinkage Prediction under a Spiked Covariance Structure: Trambak Banerjee, Gourab Mukherjee, Debashis Paul; (180):1−40, 2021.
[abs][pdf][bib]

Alibi Explain: Algorithms for Explaining Machine Learning Models: Janis Klaise, Arnaud Van Looveren, Giovanni Vacanti, Alexandru Coca; (181):1−7, 2021. (Machine Learning Open Source Software Paper)
[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; (182):1−52, 2021.
[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; (183):1−61, 2021.
[abs][pdf][bib] [code]

mlr3pipelines - Flexible Machine Learning Pipelines in R: Martin Binder, Florian Pfisterer, Michel Lang, Lennart Schneider, Lars Kotthoff, Bernd Bischl; (184):1−7, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Mode-wise Tensor Decompositions: Multi-dimensional Generalizations of CUR Decompositions: HanQin Cai, Keaton Hamm, Longxiu Huang, Deanna Needell; (185):1−36, 2021.
[abs][pdf][bib] [code]

As You Like It: Localization via Paired Comparisons: Andrew K. Massimino, Mark A. Davenport; (186):1−39, 2021.
[abs][pdf][bib]

Matrix Product States for Inference in Discrete Probabilistic Models: Rasmus Bonnevie, Mikkel N. Schmidt; (187):1−48, 2021.
[abs][pdf][bib]

Differentially Private Regression and Classification with Sparse Gaussian Processes: Michael Thomas Smith, Mauricio A. Alvarez, Neil D. Lawrence; (188):1−41, 2021.
[abs][pdf][bib] [code]

One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them: Saber Salehkaleybar, Arsalan Sharifnassab, S. Jamaloddin Golestani; (189):1−47, 2021.
[abs][pdf][bib] [code]

Collusion Detection and Ground Truth Inference in Crowdsourcing for Labeling Tasks: Changyue Song, Kaibo Liu, Xi Zhang; (190):1−45, 2021.
[abs][pdf][bib]

On the Estimation of Network Complexity: Dimension of Graphons: Yann Issartel; (191):1−62, 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; (192):1−32, 2021.
[abs][pdf][bib]

Sparse Popularity Adjusted Stochastic Block Model: Majid Noroozi, Marianna Pensky, Ramchandra Rimal; (193):1−36, 2021.
[abs][pdf][bib]

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; (194):1−59, 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; (195):1−47, 2021.
[abs][pdf][bib] [code]

COKE: Communication-Censored Decentralized Kernel Learning: Ping Xu, Yue Wang, Xiang Chen, Zhi Tian; (196):1−35, 2021.
[abs][pdf][bib]

Particle-Gibbs Sampling for Bayesian Feature Allocation Models: Alexandre Bouchard-Côté, Andrew Roth; (197):1−105, 2021.
[abs][pdf][bib] [code]

Integrated Principal Components Analysis: Tiffany M. Tang, Genevera I. Allen; (198):1−71, 2021.
[abs][pdf][bib] [code]

On ADMM in Deep Learning: Convergence and Saturation-Avoidance: Jinshan Zeng, Shao-Bo Lin, Yuan Yao, Ding-Xuan Zhou; (199):1−67, 2021.
[abs][pdf][bib]

Refined approachability algorithms and application to regret minimization with global costs: Joon Kwon; (200):1−38, 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; (201):1−73, 2021.
[abs][pdf][bib] [code]

Interpretable Deep Generative Recommendation Models: Huafeng Liu, Liping Jing, Jingxuan Wen, Pengyu Xu, Jiaqi Wang, Jian Yu, Michael K. Ng; (202):1−54, 2021.
[abs][pdf][bib]

Learning partial correlation graphs and graphical models by covariance queries: Gábor Lugosi, Jakub Truszkowski, Vasiliki Velona, Piotr Zwiernik; (203):1−41, 2021.
[abs][pdf][bib]

Failures of Model-dependent Generalization Bounds for Least-norm Interpolation: Peter L. Bartlett, Philip M. Long; (204):1−15, 2021.
[abs][pdf][bib]

Langevin Monte Carlo: random coordinate descent and variance reduction: Zhiyan Ding, Qin Li; (205):1−51, 2021.
[abs][pdf][bib]

Hamilton-Jacobi Deep Q-Learning for Deterministic Continuous-Time Systems with Lipschitz Continuous Controls: Jeongho Kim, Jaeuk Shin, Insoon Yang; (206):1−34, 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; (207):1−44, 2021.
[abs][pdf][bib]

Oblivious Data for Fairness with Kernels: Steffen Grünewälder, Azadeh Khaleghi; (208):1−36, 2021.
[abs][pdf][bib] [code]

Explaining by Removing: A Unified Framework for Model Explanation: Ian Covert, Scott Lundberg, Su-In Lee; (209):1−90, 2021.
[abs][pdf][bib] [code]

Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks: Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla; (210):1−45, 2021.
[abs][pdf][bib] [code]

Bandit Learning in Decentralized Matching Markets: Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan; (211):1−34, 2021.
[abs][pdf][bib]

Convex Geometry and Duality of Over-parameterized Neural Networks: Tolga Ergen, Mert Pilanci; (212):1−63, 2021.
[abs][pdf][bib]

Cooperative SGD: A Unified Framework for the Design and Analysis of Local-Update SGD Algorithms: Jianyu Wang, Gauri Joshi; (213):1−50, 2021.
[abs][pdf][bib]

dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python: Hubert Baniecki, Wojciech Kretowicz, Piotr Piątyszek, Jakub Wiśniewski, Przemysław Biecek; (214):1−7, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

TensorHive: Management of Exclusive GPU Access for Distributed Machine Learning Workloads: Paweł Rościszewski, Michał Martyniak, Filip Schodowski; (215):1−5, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Context-dependent Networks in Multivariate Time Series: Models, Methods, and Risk Bounds in High Dimensions: Lili Zheng, Garvesh Raskutti, Rebecca Willett, Benjamin Mark; (216):1−88, 2021.
[abs][pdf][bib]

A Unified Framework for Spectral Clustering in Sparse Graphs: Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay; (217):1−56, 2021.
[abs][pdf][bib]

Thompson Sampling Algorithms for Cascading Bandits: Zixin Zhong, Wang Chi Chueng, Vincent Y. F. Tan; (218):1−66, 2021.
[abs][pdf][bib]

Soft Tensor Regression: Georgia Papadogeorgou, Zhengwu Zhang, David B. Dunson; (219):1−53, 2021.
[abs][pdf][bib]

Shape-Enforcing Operators for Generic Point and Interval Estimators of Functions: Xi Chen, Victor Chernozhukov, Ivan Fernandez-Val, Scott Kostyshak, Ye Luo; (220):1−42, 2021.
[abs][pdf][bib]

A Bayes-Optimal View on Adversarial Examples: Eitan Richardson, Yair Weiss; (221):1−28, 2021.
[abs][pdf][bib] [code]

Classification vs regression in overparameterized regimes: Does the loss function matter?: Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel Hsu, Anant Sahai; (222):1−69, 2021.
[abs][pdf][bib]

Stochastic Online Optimization using Kalman Recursion: Joseph de Vilmarest, Olivier Wintenberger; (223):1−55, 2021.
[abs][pdf][bib]

Bayesian Distance Clustering: Leo L. Duan, David B. Dunson; (224):1−27, 2021.
[abs][pdf][bib] [code]

Representer Theorems in Banach Spaces: Minimum Norm Interpolation, Regularized Learning and Semi-Discrete Inverse Problems: Rui Wang, Yuesheng Xu; (225):1−65, 2021.
[abs][pdf][bib]

FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection: Yang Liu, Tao Fan, Tianjian Chen, Qian Xu, Qiang Yang; (226):1−6, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Tighter Risk Certificates for Neural Networks: María Pérez-Ortiz, Omar Risvaplata, John Shawe-Taylor, Csaba Szepesvári; (227):1−40, 2021.
[abs][pdf][bib] [code]

How Well Generative Adversarial Networks Learn Distributions: Tengyuan Liang; (228):1−41, 2021.
[abs][pdf][bib]

Convolutional Neural Networks Are Not Invariant to Translation, but They Can Learn to Be: Valerio Biscione, Jeffrey S. Bowers; (229):1−28, 2021.
[abs][pdf][bib]

Learning with semi-definite programming: statistical bounds based on fixed point analysis and excess risk curvature: Stéphane Chrétien, Mihai Cucuringu, Guillaume Lecué, Lucie Neirac; (230):1−64, 2021.
[abs][pdf][bib]

sklvq: Scikit Learning Vector Quantization: Rick van Veen, Michael Biehl, Gert-Jan de Vries; (231):1−6, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Probabilistic Iterative Methods for Linear Systems: Jon Cockayne, Ilse C.F. Ipsen, Chris J. Oates, Tim W. Reid; (232):1−34, 2021.
[abs][pdf][bib] [code]

A Generalised Linear Model Framework for β-Variational Autoencoders based on Exponential Dispersion Families: Robert Sicks, Ralf Korn, Stefanie Schwaar; (233):1−41, 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; (234):1−36, 2021.
[abs][pdf][bib]

GIBBON: General-purpose Information-Based Bayesian Optimisation: Henry B. Moss, David S. Leslie, Javier Gonzalez, Paul Rayson; (235):1−49, 2021.
[abs][pdf][bib]

Expanding Boundaries of Gap Safe Screening: Cassio F. Dantas, Emmanuel Soubies, Cédric Févotte; (236):1−57, 2021.
[abs][pdf][bib] [code]

Consensus-Based Optimization on the Sphere: Convergence to Global Minimizers and Machine Learning: Massimo Fornasier, Lorenzo Pareschi, Hui Huang, Philippe Sünnen; (237):1−55, 2021.
[abs][pdf][bib] [code]

DeEPCA: Decentralized Exact PCA with Linear Convergence Rate: Haishan Ye, Tong Zhang; (238):1−27, 2021.
[abs][pdf][bib]

Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo: Mert Gürbüzbalaban, Xuefeng Gao, Yuanhan Hu, Lingjiong Zhu; (239):1−69, 2021.
[abs][pdf][bib]

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; (240):1−9, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

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; (241):1−124, 2021.
[abs][pdf][bib] [code]

Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations: Jesus Maria Sanz-Serna, Konstantinos C. Zygalakis; (242):1−37, 2021.
[abs][pdf][bib]

Quasi-Monte Carlo Quasi-Newton in Variational Bayes: Sifan Liu, Art B. Owen; (243):1−23, 2021.
[abs][pdf][bib]

Consistency of Gaussian Process Regression in Metric Spaces: Peter Koepernik, Florian Pfaff; (244):1−27, 2021.
[abs][pdf][bib]

On lp-hyperparameter Learning via Bilevel Nonsmooth Optimization: Takayuki Okuno, Akiko Takeda, Akihiro Kawana, Motokazu Watanabe; (245):1−47, 2021.
[abs][pdf][bib]

Mixture Martingales Revisited with Applications to Sequential Tests and Confidence Intervals: Emilie Kaufmann, Wouter M. Koolen; (246):1−44, 2021.
[abs][pdf][bib]

Statistical Guarantees for Local Spectral Clustering on Random Neighborhood Graphs: Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani; (247):1−71, 2021.
[abs][pdf][bib]

Statistically and Computationally Efficient Change Point Localization in Regression Settings: Daren Wang, Zifeng Zhao, Kevin Z. Lin, Rebecca Willett; (248):1−46, 2021.
[abs][pdf][bib]

On the Riemannian Search for Eigenvector Computation: Zhiqiang Xu, Ping Li; (249):1−46, 2021.
[abs][pdf][bib]

Bayesian time-aligned factor analysis of paired multivariate time series: Arkaprava Roy, Jana Schaich Borg, David B Dunson; (250):1−27, 2021.
[abs][pdf][bib]

Tractable Approximate Gaussian Inference for Bayesian Neural Networks: James-A. Goulet, Luong Ha Nguyen, Saeid Amiri; (251):1−23, 2021.
[abs][pdf][bib] [code]

Batch greedy maximization of non-submodular functions: Guarantees and applications to experimental design: Jayanth Jagalur-Mohan, Youssef Marzouk; (252):1−62, 2021.
[abs][pdf][bib]

Bifurcation Spiking Neural Network: Shao-Qun Zhang, Zhao-Yu Zhang, Zhi-Hua Zhou; (253):1−21, 2021.
[abs][pdf][bib]

Inference for the Case Probability in High-dimensional Logistic Regression: Zijian Guo, Prabrisha Rakshit, Daniel S. Herman, Jinbo Chen; (254):1−54, 2021.
[abs][pdf][bib]

Adversarial Monte Carlo Meta-Learning of Optimal Prediction Procedures: Alex Luedtke, Incheoul Chung, Oleg Sofrygin; (255):1−67, 2021.
[abs][pdf][bib] [code]

Model Linkage Selection for Cooperative Learning: Jiaying Zhou, Jie Ding, Kean Ming Tan, Vahid Tarokh; (256):1−44, 2021.
[abs][pdf][bib]

Estimating Uncertainty Intervals from Collaborating Networks: Tianhui Zhou, Yitong Li, Yuan Wu, David Carlson; (257):1−47, 2021.
[abs][pdf][bib]

Optimized Score Transformation for Consistent Fair Classification: Dennis Wei, Karthikeyan Natesan Ramamurthy, Flavio P. Calmon; (258):1−78, 2021.
[abs][pdf][bib]

ROOTS: Object-Centric Representation and Rendering of 3D Scenes: Chang Chen, Fei Deng, Sungjin Ahn; (259):1−36, 2021.
[abs][pdf][bib]

Learning Strategies in Decentralized Matching Markets under Uncertain Preferences: Xiaowu Dai, Michael I. Jordan; (260):1−50, 2021.
[abs][pdf][bib]

Domain adaptation under structural causal models: Yuansi Chen, Peter Bühlmann; (261):1−80, 2021.
[abs][pdf][bib] [code]

Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning: Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang; (262):1−44, 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; (263):1−77, 2021.
[abs][pdf][bib]

Regularized spectral methods for clustering signed networks: Mihai Cucuringu, Apoorv Vikram Singh, Déborah Sulem, Hemant Tyagi; (264):1−79, 2021.
[abs][pdf][bib]

Exact Asymptotics for Linear Quadratic Adaptive Control: Feicheng Wang, Lucas Janson; (265):1−112, 2021.
[abs][pdf][bib] [code]

Learning Bayesian Networks from Ordinal Data: Xiang Ge Luo, Giusi Moffa, Jack Kuipers; (266):1−44, 2021.
[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; (267):1−56, 2021.
[abs][pdf][bib]

Stable-Baselines3: Reliable Reinforcement Learning Implementations: Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, Noah Dormann; (268):1−8, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

CAT: Compression-Aware Training for bandwidth reduction: Chaim Baskin, Brian Chmiel, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, Avi Mendelson; (269):1−20, 2021.
[abs][pdf][bib] [code]

Further results on latent discourse models and word embeddings: Sammy Khalife, Douglas Gonçalves, Youssef Allouah, Leo Liberti; (270):1−36, 2021.
[abs][pdf][bib]

Nonparametric Continuous Sensor Registration: William Clark, Maani Ghaffari, Anthony Bloch; (271):1−50, 2021.
[abs][pdf][bib] [code]

Transferability of Spectral Graph Convolutional Neural Networks: Ron Levie, Wei Huang, Lorenzo Bucci, Michael Bronstein, Gitta Kutyniok; (272):1−59, 2021.
[abs][pdf][bib]

On the Hardness of Robust Classification: Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell; (273):1−29, 2021.
[abs][pdf][bib]

Simultaneous Change Point Inference and Structure Recovery for High Dimensional Gaussian Graphical Models: Bin Liu, Xinsheng Zhang, Yufeng Liu; (274):1−62, 2021.
[abs][pdf][bib]

Partial Policy Iteration for L1-Robust Markov Decision Processes: Chin Pang Ho, Marek Petrik, Wolfram Wiesemann; (275):1−46, 2021.
[abs][pdf][bib] [code]

Estimating the Lasso's Effective Noise: Johannes Lederer, Michael Vogt; (276):1−32, 2021.
[abs][pdf][bib] [supplementary]

Gaussian Approximation for Bias Reduction in Q-Learning: Carlo D'Eramo, Andrea Cini, Alessandro Nuara, Matteo Pirotta, Cesare Alippi, Jan Peters, Marcello Restelli; (277):1−51, 2021.
[abs][pdf][bib]

Multilevel Monte Carlo Variational Inference: Masahiro Fujisawa, Issei Sato; (278):1−44, 2021.
[abs][pdf][bib]

Fast Learning for Renewal Optimization in Online Task Scheduling: Michael J. Neely; (279):1−44, 2021.
[abs][pdf][bib]

Graph Matching with Partially-Correct Seeds: Liren Yu, Jiaming Xu, Xiaojun Lin; (280):1−54, 2021.
[abs][pdf][bib] [code]

Contrastive Estimation Reveals Topic Posterior Information to Linear Models: Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu; (281):1−31, 2021.
[abs][pdf][bib]

LDLE: Low Distortion Local Eigenmaps: Dhruv Kohli, Alexander Cloninger, Gal Mishne; (282):1−64, 2021.
[abs][pdf][bib] [code]

Non-linear, Sparse Dimensionality Reduction via Path Lasso Penalized Autoencoders: Oskar Allerbo, Rebecka Jörnsten; (283):1−28, 2021.
[abs][pdf][bib] [code]

Linear Bandits on Uniformly Convex Sets: Thomas Kerdreux, Christophe Roux, Alexandre d'Aspremont, Sebastian Pokutta; (284):1−23, 2021.
[abs][pdf][bib]

Double Generative Adversarial Networks for Conditional Independence Testing: Chengchun Shi, Tianlin Xu, Wicher Bergsma, Lexin Li; (285):1−32, 2021.
[abs][pdf][bib]

An Online Sequential Test for Qualitative Treatment Effects: Chengchun Shi, Shikai Luo, Hongtu Zhu, Rui Song; (286):1−51, 2021.
[abs][pdf][bib]

V-statistics and Variance Estimation: Zhengze Zhou, Lucas Mentch, Giles Hooker; (287):1−48, 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; (288):1−38, 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; (289):1−39, 2021.
[abs][pdf][bib] [code]

On Universal Approximation and Error Bounds for Fourier Neural Operators: Nikola Kovachki, Samuel Lanthaler, Siddhartha Mishra; (290):1−76, 2021.
[abs][pdf][bib]