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JMLR Volume 21

A Low Complexity Algorithm with O(√T) Regret and O(1) Constraint Violations for Online Convex Optimization with Long Term Constraints
Hao Yu, Michael J. Neely; (1):1−24, 2020.
[abs][pdf][bib]

A Statistical Learning Approach to Modal Regression
Yunlong Feng, Jun Fan, Johan A.K. Suykens; (2):1−35, 2020.
[abs][pdf][bib]

A Model of Fake Data in Data-driven Analysis
Xiaofan Li, Andrew B. Whinston; (3):1−26, 2020.
[abs][pdf][bib]

Universal Latent Space Model Fitting for Large Networks with Edge Covariates
Zhuang Ma, Zongming Ma, Hongsong Yuan; (4):1−67, 2020.
[abs][pdf][bib]

Lower Bounds for Parallel and Randomized Convex Optimization
Jelena Diakonikolas, Cristóbal Guzmán; (5):1−31, 2020.
[abs][pdf][bib]

Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms
Anna Little, Mauro Maggioni, James M. Murphy; (6):1−66, 2020.
[abs][pdf][bib]      [code]

Target Propagation in Recurrent Neural Networks
Nikolay Manchev, Michael Spratling; (7):1−33, 2020.
[abs][pdf][bib]      [code]

DESlib: A Dynamic ensemble selection library in Python
Rafael M. O. Cruz, Luiz G. Hafemann, Robert Sabourin, George D. C. Cavalcanti; (8):1−5, 2020.
[abs][pdf][bib]      [code]

On Mahalanobis Distance in Functional Settings
José R. Berrendero, Beatriz Bueno-Larraz, Antonio Cuevas; (9):1−33, 2020.
[abs][pdf][bib]

Online Sufficient Dimension Reduction Through Sliced Inverse Regression
Zhanrui Cai, Runze Li, Liping Zhu; (10):1−25, 2020.
[abs][pdf][bib]

Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information
T. Tony Cai, Tengyuan Liang, Alexander Rakhlin; (11):1−34, 2020.
[abs][pdf][bib]

Neyman-Pearson classification: parametrics and sample size requirement
Xin Tong, Lucy Xia, Jiacheng Wang, Yang Feng; (12):1−48, 2020.
[abs][pdf][bib]      [code]

Generalized probabilistic principal component analysis of correlated data
Mengyang Gu, Weining Shen; (13):1−41, 2020.
[abs][pdf][bib]      [code]

On lp-Support Vector Machines and Multidimensional Kernels
Victor Blanco, Justo Puerto, Antonio M. Rodriguez-Chia; (14):1−29, 2020.
[abs][pdf][bib]

Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning
Ery Arias-Castro, Adel Javanmard, Bruno Pelletier; (15):1−37, 2020.
[abs][pdf][bib]

Practical Locally Private Heavy Hitters
Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta; (16):1−42, 2020.
[abs][pdf][bib]

Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data
Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian P. Robert; (17):1−53, 2020.
[abs][pdf][bib]

Connecting Spectral Clustering to Maximum Margins and Level Sets
David P. Hofmeyr; (18):1−35, 2020.
[abs][pdf][bib]

High-Dimensional Interactions Detection with Sparse Principal Hessian Matrix
Cheng Yong Tang, Ethan X. Fang, Yuexiao Dong; (19):1−25, 2020.
[abs][pdf][bib]

Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections
Junhong Lin, Volkan Cevher; (20):1−44, 2020.
[abs][pdf][bib]

Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems
Dhruv Malik, Ashwin Pananjady, Kush Bhatia, Koulik Khamaru, Peter L. Bartlett, Martin J. Wainwright; (21):1−51, 2020.
[abs][pdf][bib]

A Unified Framework for Structured Graph Learning via Spectral Constraints
Sandeep Kumar, Jiaxi Ying, José Vinícius de M. Cardoso, Daniel P. Palomar; (22):1−60, 2020.
[abs][pdf][bib]      [code]

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing
Jian Guo, He He, Tong He, Leonard Lausen, Mu Li, Haibin Lin, Xingjian Shi, Chenguang Wang, Junyuan Xie, Sheng Zha, Aston Zhang, Hang Zhang, Zhi Zhang, Zhongyue Zhang, Shuai Zheng, Yi Zhu; (23):1−7, 2020.
[abs][pdf][bib]

Distributed Feature Screening via Componentwise Debiasing
Xingxiang Li, Runze Li, Zhiming Xia, Chen Xu; (24):1−32, 2020.
[abs][pdf][bib]

Lower Bounds for Testing Graphical Models: Colorings and Antiferromagnetic Ising Models
Ivona Bezáková, Antonio Blanca, Zongchen Chen, Daniel Štefankovič, Eric Vigoda; (25):1−62, 2020.
[abs][pdf][bib]

Targeted Fused Ridge Estimation of Inverse Covariance Matrices from Multiple High-Dimensional Data Classes
Anders Ellern Bilgrau, Carel F.W. Peeters, Poul Svante Eriksen, Martin Boegsted, Wessel N. van Wieringen; (26):1−52, 2020.
[abs][pdf][bib]

A New Class of Time Dependent Latent Factor Models with Applications
Sinead A. Williamson, Michael Minyi Zhang, Paul Damien; (27):1−24, 2020.
[abs][pdf][bib]

On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms
Nicolas Garcia Trillos, Zachary Kaplan, Thabo Samakhoana, Daniel Sanz-Alonso; (28):1−47, 2020.
[abs][pdf][bib]

The Maximum Separation Subspace in Sufficient Dimension Reduction with Categorical Response
Xin Zhang, Qing Mai, Hui Zou; (29):1−36, 2020.
[abs][pdf][bib]

Tensor Train Decomposition on TensorFlow (T3F)
Alexander Novikov, Pavel Izmailov, Valentin Khrulkov, Michael Figurnov, Ivan Oseledets; (30):1−7, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Generalized Nonbacktracking Bounds on the Influence
Emmanuel Abbe, Sanjeev Kulkarni, Eun Jee Lee; (31):1−36, 2020.
[abs][pdf][bib]

Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping
Mihai Cucuringu, Hemant Tyagi; (32):1−77, 2020.
[abs][pdf][bib]

On the Complexity Analysis of the Primal Solutions for the Accelerated Randomized Dual Coordinate Ascent
Huan Li, Zhouchen Lin; (33):1−45, 2020.
[abs][pdf][bib]

Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent
Dominic Richards, Patrick Rebeschini; (34):1−44, 2020.
[abs][pdf][bib]

Learning with Fenchel-Young losses
Mathieu Blondel, André F.T. Martins, Vlad Niculae; (35):1−69, 2020.
[abs][pdf][bib]      [code]

Noise Accumulation in High Dimensional Classification and Total Signal Index
Miriam R. Elman, Jessica Minnier, Xiaohui Chang, Dongseok Choi; (36):1−23, 2020.
[abs][pdf][bib]      [code]

Causal Discovery Toolbox: Uncovering causal relationships in Python
Diviyan Kalainathan, Olivier Goudet, Ritik Dutta; (37):1−5, 2020.
[abs][pdf][bib]      [code]

Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification
Leo L. Duan; (38):1−25, 2020.
[abs][pdf][bib]      [code]

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables
Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash, Kun Zhang; (39):1−24, 2020.
[abs][pdf][bib]

Optimal Bipartite Network Clustering
Zhixin Zhou, Arash A. Amini; (40):1−68, 2020.
[abs][pdf][bib]

Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables
Rune Christiansen, Jonas Peters; (41):1−46, 2020.
[abs][pdf][bib]      [code]

Branch and Bound for Piecewise Linear Neural Network Verification
Rudy Bunel, Jingyue Lu, Ilker Turkaslan, Philip H.S. Torr, Pushmeet Kohli, M. Pawan Kumar; (42):1−39, 2020.
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Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data
Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan; (43):1−36, 2020.
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Dynamical Systems as Temporal Feature Spaces
Peter Tino; (44):1−42, 2020.
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A Convex Parametrization of a New Class of Universal Kernel Functions
Brendon K. Colbert, Matthew M. Peet; (45):1−29, 2020.
[abs][pdf][bib]

pyts: A Python Package for Time Series Classification
Johann Faouzi, Hicham Janati; (46):1−6, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement
Wouter Kool, Herke van Hoof, Max Welling; (47):1−36, 2020.
[abs][pdf][bib]      [code]

Skill Rating for Multiplayer Games. Introducing Hypernode Graphs and their Spectral Theory
Thomas Ricatte, Rémi Gilleron, Marc Tommasi; (48):1−18, 2020.
[abs][pdf][bib]

Ensemble Learning for Relational Data
Hoda Eldardiry, Jennifer Neville, Ryan A. Rossi; (49):1−37, 2020.
[abs][pdf][bib]

Sparse and low-rank multivariate Hawkes processes
Emmanuel Bacry, Martin Bompaire, Stéphane Gaïffas, Jean-Francois Muzy; (50):1−32, 2020.
[abs][pdf][bib]      [code]

Learning Causal Networks via Additive Faithfulness
Kuang-Yao Lee, Tianqi Liu, Bing Li, Hongyu Zhao; (51):1−38, 2020.
[abs][pdf][bib]

Expected Policy Gradients for Reinforcement Learning
Kamil Ciosek, Shimon Whiteson; (52):1−51, 2020.
[abs][pdf][bib]

High-Dimensional Inference for Cluster-Based Graphical Models
Carson Eisenach, Florentina Bunea, Yang Ning, Claudiu Dinicu; (53):1−55, 2020.
[abs][pdf][bib]

GraKeL: A Graph Kernel Library in Python
Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis, Michalis Vazirgiannis; (54):1−5, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Conjugate Gradients for Kernel Machines
Simon Bartels, Philipp Hennig; (55):1−42, 2020.
[abs][pdf][bib]

Fast Rates for General Unbounded Loss Functions: From ERM to Generalized Bayes
Peter D. Grünwald, Nishant A. Mehta; (56):1−80, 2020.
[abs][pdf][bib]

Self-paced Multi-view Co-training
Fan Ma, Deyu Meng, Xuanyi Dong, Yi Yang; (57):1−38, 2020.
[abs][pdf][bib]      [code]

Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions
Artin Spiridonoff, Alex Olshevsky, Ioannis Ch. Paschalidis; (58):1−47, 2020.
[abs][pdf][bib]

Exact Guarantees on the Absence of Spurious Local Minima for Non-negative Rank-1 Robust Principal Component Analysis
Salar Fattahi, Somayeh Sojoudi; (59):1−51, 2020.
[abs][pdf][bib]

Kymatio: Scattering Transforms in Python
Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim Andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Muawiz Chaudhary, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine Cella, Michael Eickenberg; (60):1−6, 2020. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Multiparameter Persistence Landscapes
Oliver Vipond; (61):1−38, 2020.
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Generalized Optimal Matching Methods for Causal Inference
Nathan Kallus; (62):1−54, 2020.
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Unique Sharp Local Minimum in L1-minimization Complete Dictionary Learning
Yu Wang, Siqi Wu, Bin Yu; (63):1−52, 2020.
[abs][pdf][bib]

Community-Based Group Graphical Lasso
Eugen Pircalabelu, Gerda Claeskens; (64):1−32, 2020.
[abs][pdf][bib]

Smoothed Nonparametric Derivative Estimation using Weighted Difference Quotients
Yu Liu, Kris De Brabanter; (65):1−45, 2020.
[abs][pdf][bib]

WONDER: Weighted One-shot Distributed Ridge Regression in High Dimensions
Edgar Dobriban, Yue Sheng; (66):1−52, 2020.
[abs][pdf][bib]      [code]

The weight function in the subtree kernel is decisive
Romain Azaïs, Florian Ingels; (67):1−36, 2020.
[abs][pdf][bib]      [code]

On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics
Xi Chen, Simon S. Du, Xin T. Tong; (68):1−41, 2020.
[abs][pdf][bib]

Union of Low-Rank Tensor Spaces: Clustering and Completion
Morteza Ashraphijuo, Xiaodong Wang; (69):1−36, 2020.
[abs][pdf][bib]

Representation Learning for Dynamic Graphs: A Survey
Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, Pascal Poupart; (70):1−73, 2020.
[abs][pdf][bib]

Estimation of a Low-rank Topic-Based Model for Information Cascades
Ming Yu, Varun Gupta, Mladen Kolar; (71):1−47, 2020.
[abs][pdf][bib]      [code]

(1 + epsilon)-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets
Maxim Borisyak, Artem Ryzhikov, Andrey Ustyuzhanin, Denis Derkach, Fedor Ratnikov, Olga Mineeva; (72):1−22, 2020.
[abs][pdf][bib]      [code]

Scalable Approximate MCMC Algorithms for the Horseshoe Prior
James Johndrow, Paulo Orenstein, Anirban Bhattacharya; (73):1−61, 2020.
[abs][pdf][bib]

High-dimensional Gaussian graphical models on network-linked data
Tianxi Li, Cheng Qian, Elizaveta Levina, Ji Zhu; (74):1−45, 2020.
[abs][pdf][bib]      [code]

Identifiability of Additive Noise Models Using Conditional Variances
Gunwoong Park; (75):1−34, 2020.
[abs][pdf][bib]

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning
Anis Elgabli, Jihong Park, Amrit S. Bedi, Mehdi Bennis, Vaneet Aggarwal; (76):1−39, 2020.
[abs][pdf][bib]

Multi-Player Bandits: The Adversarial Case
Pragnya Alatur, Kfir Y. Levy, Andreas Krause; (77):1−23, 2020.
[abs][pdf][bib]

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