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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
Xiao Di, 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]

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.
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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.
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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]

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