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JMLR Volume 20

Adaptation Based on Generalized Discrepancy: Corinna Cortes, Mehryar Mohri, Andrés Muñoz Medina; (1):1−30, 2019.
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Transport Analysis of Infinitely Deep Neural Network: Sho Sonoda, Noboru Murata; (2):1−52, 2019.
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Parsimonious Online Learning with Kernels via Sparse Projections in Function Space: Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro Ribeiro; (3):1−44, 2019.
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Convergence Rate of a Simulated Annealing Algorithm with Noisy Observations: Clément Bouttier, Ioana Gavra; (4):1−45, 2019.
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Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression: Han Chen, Garvesh Raskutti, Ming Yuan; (5):1−37, 2019.
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scikit-multilearn: A Python library for Multi-Label Classification: Piotr Szymański, Tomasz Kajdanowicz; (6):1−22, 2019. (Machine Learning Open Source Software Paper)
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Scalable Approximations for Generalized Linear Problems: Murat Erdogdu, Mohsen Bayati, Lee H. Dicker; (7):1−45, 2019.
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Forward-Backward Selection with Early Dropping: Giorgos Borboudakis, Ioannis Tsamardinos; (8):1−39, 2019.
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Dynamic Pricing in High-dimensions: Adel Javanmard, Hamid Nazerzadeh; (9):1−49, 2019.
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Graphical Lasso and Thresholding: Equivalence and Closed-form Solutions: Salar Fattahi, Somayeh Sojoudi; (10):1−44, 2019.
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An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory: Mehmet Eren Ahsen, Mathukumalli Vidyasagar; (11):1−23, 2019.
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Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds: Shusen Wang, Alex Gittens, Michael W. Mahoney; (12):1−49, 2019.
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Train and Test Tightness of LP Relaxations in Structured Prediction: Ofer Meshi, Ben London, Adrian Weller, David Sontag; (13):1−34, 2019.
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Approximations of the Restless Bandit Problem: Steffen Grünewälder, Azadeh Khaleghi; (14):1−37, 2019.
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Automated Scalable Bayesian Inference via Hilbert Coresets: Trevor Campbell, Tamara Broderick; (15):1−38, 2019.
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Smooth neighborhood recommender systems: Ben Dai, Junhui Wang, Xiaotong Shen, Annie Qu; (16):1−24, 2019.
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Delay and Cooperation in Nonstochastic Bandits: Nicolò Cesa-Bianchi, Claudio Gentile, Yishay Mansour; (17):1−38, 2019.
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Multiplicative local linear hazard estimation and best one-sided cross-validation: Maria Luz Gámiz, María Dolores Martínez-Miranda, Jens Perch Nielsen; (18):1−29, 2019.
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spark-crowd: A Spark Package for Learning from Crowdsourced Big Data: Enrique G. Rodrigo, Juan A. Aledo, José A. Gámez; (19):1−5, 2019. (Machine Learning Open Source Software Paper)
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Accelerated Alternating Projections for Robust Principal Component Analysis: HanQin Cai, Jian-Feng Cai, Ke Wei; (20):1−33, 2019.
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Spectrum Estimation from a Few Entries: Ashish Khetan, Sewoong Oh; (21):1−55, 2019.
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Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics: Yanning Shen, Tianyi Chen, Georgios B. Giannakis; (22):1−36, 2019.
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Determining the Number of Latent Factors in Statistical Multi-Relational Learning: Chengchun Shi, Wenbin Lu, Rui Song; (23):1−38, 2019.
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Joint PLDA for Simultaneous Modeling of Two Factors: Luciana Ferrer, Mitchell McLaren; (24):1−29, 2019.
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Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations: Alberto Bietti, Julien Mairal; (25):1−49, 2019.
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TensorLy: Tensor Learning in Python: Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic; (26):1−6, 2019. (Machine Learning Open Source Software Paper)
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Monotone Learning with Rectified Wire Networks: Veit Elser, Dan Schmidt, Jonathan Yedidia; (27):1−42, 2019.
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Pyro: Deep Universal Probabilistic Programming: Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul Szerlip, Paul Horsfall, Noah D. Goodman; (28):1−6, 2019. (Machine Learning Open Source Software Paper)
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Iterated Learning in Dynamic Social Networks: Bernard Chazelle, Chu Wang; (29):1−28, 2019.
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Exact Clustering of Weighted Graphs via Semidefinite Programming: Aleksis Pirinen, Brendan Ames; (30):1−34, 2019.
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Kernels for Sequentially Ordered Data: Franz J. Kiraly, Harald Oberhauser; (31):1−45, 2019.
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NetSDM: Semantic Data Mining with Network Analysis: Jan Kralj, Marko Robnik-Sikonja, Nada Lavrac; (32):1−50, 2019.
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The Relationship Between Agnostic Selective Classification, Active Learning and the Disagreement Coefficient: Roei Gelbhart, Ran El-Yaniv; (33):1−38, 2019.
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Matched Bipartite Block Model with Covariates: Zahra S. Razaee, Arash A. Amini, Jingyi Jessica Li; (34):1−44, 2019.
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Optimal Policies for Observing Time Series and Related Restless Bandit Problems: Christopher R. Dance, Tomi Silander; (35):1−93, 2019.
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A New Approach to Laplacian Solvers and Flow Problems: Patrick Rebeschini, Sekhar Tatikonda; (36):1−37, 2019.
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A Well-Tempered Landscape for Non-convex Robust Subspace Recovery: Tyler Maunu, Teng Zhang, Gilad Lerman; (37):1−59, 2019.
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Approximation Hardness for A Class of Sparse Optimization Problems: Yichen Chen, Yinyu Ye, Mengdi Wang; (38):1−27, 2019.
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A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication: Miles E. Lopes, Shusen Wang, Michael W. Mahoney; (39):1−40, 2019.
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Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations: Qianxiao Li, Cheng Tai, Weinan E; (40):1−47, 2019.
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Decontamination of Mutual Contamination Models: Julian Katz-Samuels, Gilles Blanchard, Clayton Scott; (41):1−57, 2019.
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Utilizing Second Order Information in Minibatch Stochastic Variance Reduced Proximal Iterations: Jialei Wang, Tong Zhang; (42):1−56, 2019.
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DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization: Lin Xiao, Adams Wei Yu, Qihang Lin, Weizhu Chen; (43):1−58, 2019.
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Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python: Jason Ge, Xingguo Li, Haoming Jiang, Han Liu, Tong Zhang, Mengdi Wang, Tuo Zhao; (44):1−5, 2019. (Machine Learning Open Source Software Paper)
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Robust Frequent Directions with Application in Online Learning: Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li, Tong Zhang; (45):1−41, 2019.
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Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping: Shao-Bo Lin, Yunwen Lei, Ding-Xuan Zhou; (46):1−36, 2019.
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Analysis of spectral clustering algorithms for community detection: the general bipartite setting: Zhixin Zhou, Arash A.Amini; (47):1−47, 2019.
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Efficient augmentation and relaxation learning for individualized treatment rules using observational data: Ying-Qi Zhao, Eric B. Laber, Yang Ning, Sumona Saha, Bruce E. Sands; (48):1−23, 2019.
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Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots: Akshara Rai, Rika Antonova, Franziska Meier, Christopher G. Atkeson; (49):1−24, 2019.
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No-Regret Bayesian Optimization with Unknown Hyperparameters: Felix Berkenkamp, Angela P. Schoellig, Andreas Krause; (50):1−24, 2019.
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Bayesian Combination of Probabilistic Classifiers using Multivariate Normal Mixtures: Gregor Pirš, Erik Štrumbelj; (51):1−18, 2019.
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Thompson Sampling Guided Stochastic Searching on the Line for Deceptive Environments with Applications to Root-Finding Problems: Sondre Glimsdal, Ole-Christoffer Granmo; (52):1−24, 2019.
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Tunability: Importance of Hyperparameters of Machine Learning Algorithms: Philipp Probst, Anne-Laure Boulesteix, Bernd Bischl; (53):1−32, 2019.
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Deep Reinforcement Learning for Swarm Systems: Maximilian Hüttenrauch, Adrian Šošić, Gerhard Neumann; (54):1−31, 2019.
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Neural Architecture Search: A Survey: Thomas Elsken, Jan Hendrik Metzen, Frank Hutter; (55):1−21, 2019.
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Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices: Zengfeng Huang; (56):1−23, 2019.
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Multi-class Heterogeneous Domain Adaptation: Joey Tianyi Zhou, Ivor W. Tsang, Sinno Jialin Pan, Mingkui Tan; (57):1−31, 2019.
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The Common-directions Method for Regularized Empirical Risk Minimization: Po-Wei Wang, Ching-pei Lee, Chih-Jen Lin; (58):1−49, 2019.
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Kernel Approximation Methods for Speech Recognition: Avner May, Alireza Bagheri Garakani, Zhiyun Lu, Dong Guo, Kuan Liu, Aurélien Bellet, Linxi Fan, Michael Collins, Daniel Hsu, Brian Kingsbury, Michael Picheny, Fei Sha; (59):1−36, 2019.
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Robust Estimation of Derivatives Using Locally Weighted Least Absolute Deviation Regression: WenWu Wang, Ping Yu, Lu Lin, Tiejun Tong; (60):1−49, 2019.
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The Sup-norm Perturbation of HOSVD and Low Rank Tensor Denoising: Dong Xia, Fan Zhou; (61):1−42, 2019.
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Multi-scale Online Learning: Theory and Applications to Online Auctions and Pricing: Sébastien Bubeck, Nikhil R. Devanur, Zhiyi Huang, Rad Niazadeh; (62):1−37, 2019.
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Nearly-tight VC-dimension and Pseudodimension Bounds for Piecewise Linear Neural Networks: Peter L. Bartlett, Nick Harvey, Christopher Liaw, Abbas Mehrabian; (63):1−17, 2019.
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A Representer Theorem for Deep Kernel Learning: Bastian Bohn, Michael Griebel, Christian Rieger; (64):1−32, 2019.
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Active Learning for Cost-Sensitive Classification: Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford; (65):1−50, 2019.
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Proximal Distance Algorithms: Theory and Practice: Kevin L. Keys, Hua Zhou, Kenneth Lange; (66):1−38, 2019.
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Learnability of Solutions to Conjunctive Queries: Hubie Chen, Matthew Valeriote; (67):1−28, 2019.
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Variance-based Regularization with Convex Objectives: John Duchi, Hongseok Namkoong; (68):1−55, 2019.
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On Consistent Vertex Nomination Schemes: Vince Lyzinski, Keith Levin, Carey E. Priebe; (69):1−39, 2019.
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Semi-Analytic Resampling in Lasso: Tomoyuki Obuchi, Yoshiyuki Kabashima; (70):1−33, 2019.
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Lazifying Conditional Gradient Algorithms: Gábor Braun, Sebastian Pokutta, Daniel Zink; (71):1−42, 2019.
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Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning: Can Karakus, Yifan Sun, Suhas Diggavi, Wotao Yin; (72):1−47, 2019.
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Analysis of Langevin Monte Carlo via Convex Optimization: Alain Durmus, Szymon Majewski, Błażej Miasojedow; (73):1−46, 2019.
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Deep Optimal Stopping: Sebastian Becker, Patrick Cheridito, Arnulf Jentzen; (74):1−25, 2019.
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Fairness Constraints: A Flexible Approach for Fair Classification: Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi; (75):1−42, 2019.
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Generalized Score Matching for Non-Negative Data: Shiqing Yu, Mathias Drton, Ali Shojaie; (76):1−70, 2019.
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Nonuniformity of P-values Can Occur Early in Diverging Dimensions: Yingying Fan, Emre Demirkaya, Jinchi Lv; (77):1−33, 2019.
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Prediction Risk for the Horseshoe Regression: Anindya Bhadra, Jyotishka Datta, Yunfan Li, Nicholas G. Polson, Brandon Willard; (78):1−39, 2019.
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Quantification Under Prior Probability Shift: the Ratio Estimator and its Extensions: Afonso Fernandes Vaz, Rafael Izbicki, Rafael Bassi Stern; (79):1−33, 2019.
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Learning to Match via Inverse Optimal Transport: Ruilin Li, Xiaojing Ye, Haomin Zhou, Hongyuan Zha; (80):1−37, 2019.
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Tight Lower Bounds on the VC-dimension of Geometric Set Systems: Mónika Csikós, Nabil H. Mustafa, Andrey Kupavskii; (81):1−8, 2019.
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SMART: An Open Source Data Labeling Platform for Supervised Learning: Rob Chew, Michael Wenger, Caroline Kery, Jason Nance, Keith Richards, Emily Hadley, Peter Baumgartner; (82):1−5, 2019. (Machine Learning Open Source Software Paper)
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On the optimality of the Hedge algorithm in the stochastic regime: Jaouad Mourtada, Stéphane Gaïffas; (83):1−28, 2019.
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Differentiable Game Mechanics: Alistair Letcher, David Balduzzi, Sébastien Racanière, James Martens, Jakob Foerster, Karl Tuyls, Thore Graepel; (84):1−40, 2019.
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Bayesian Space-Time Partitioning by Sampling and Pruning Spanning Trees: Leonardo V. Teixeira, Renato M. Assunção, Rosangela H. Loschi; (85):1−35, 2019.
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Streaming Principal Component Analysis From Incomplete Data: Armin Eftekhari, Gregory Ongie, Laura Balzano, Michael B. Wakin; (86):1−62, 2019.
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An asymptotic analysis of distributed nonparametric methods: Botond Szabó, Harry van Zanten; (87):1−30, 2019.
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Model Selection via the VC Dimension: Merlin Mpoudeu, Bertrand Clarke; (88):1−26, 2019.
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Dependent relevance determination for smooth and structured sparse regression: Anqi Wu, Oluwasanmi Koyejo, Jonathan Pillow; (89):1−43, 2019.
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A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization: Muhammad A Masood, Finale Doshi-Velez; (90):1−56, 2019.
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Best Arm Identification for Contaminated Bandits: Jason Altschuler, Victor-Emmanuel Brunel, Alan Malek; (91):1−39, 2019.
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AffectiveTweets: a Weka Package for Analyzing Affect in Tweets: Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer, Saif M. Mohammad; (92):1−6, 2019. (Machine Learning Open Source Software Paper)
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iNNvestigate Neural Networks!: Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans; (93):1−8, 2019. (Machine Learning Open Source Software Paper)
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Simultaneous Private Learning of Multiple Concepts: Mark Bun, Kobbi Nissim, Uri Stemmer; (94):1−34, 2019.
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High-Dimensional Poisson Structural Equation Model Learning via $\ell_1$-Regularized Regression: Gunwoong Park, Sion Park; (95):1−41, 2019.
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PyOD: A Python Toolbox for Scalable Outlier Detection: Yue Zhao, Zain Nasrullah, Zheng Li; (96):1−7, 2019. (Machine Learning Open Source Software Paper)
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Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion: Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou; (97):1−22, 2019.
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Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data: Wenjing Liao, Mauro Maggioni; (98):1−63, 2019.
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Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction: William Herlands, Daniel B. Neill, Hannes Nickisch, Andrew Gordon Wilson; (99):1−51, 2019.
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Hamiltonian Monte Carlo with Energy Conserving Subsampling: Khue-Dung Dang, Matias Quiroz, Robert Kohn, Minh-Ngoc Tran, Mattias Villani; (100):1−31, 2019.
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Low Permutation-rank Matrices: Structural Properties and Noisy Completion: Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright; (101):1−43, 2019.
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Non-Convex Matrix Completion and Related Problems via Strong Duality: Maria-Florina Balcan, Yingyu Liang, Zhao Song, David P. Woodruff, Hongyang Zhang; (102):1−56, 2019.
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Regularization via Mass Transportation: Soroosh Shafieezadeh-Abadeh, Daniel Kuhn, Peyman Mohajerin Esfahani; (103):1−68, 2019.
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Complete Search for Feature Selection in Decision Trees: Salvatore Ruggieri; (104):1−34, 2019.
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Optimal Transport: Fast Probabilistic Approximation with Exact Solvers: Max Sommerfeld, Jörn Schrieber, Yoav Zemel, Axel Munk; (105):1−23, 2019.
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Solving the OSCAR and SLOPE Models Using a Semismooth Newton-Based Augmented Lagrangian Method: Ziyan Luo, Defeng Sun, Kim-Chuan Toh, Naihua Xiu; (106):1−25, 2019.
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Scalable Interpretable Multi-Response Regression via SEED: Zemin Zheng, M. Taha Bahadori, Yan Liu, Jinchi Lv; (107):1−34, 2019.
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Maximum Likelihood for Gaussian Process Classification and Generalized Linear Mixed Models under Case-Control Sampling: Omer Weissbrod, Shachar Kaufman, David Golan, Saharon Rosset; (108):1−30, 2019.
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Learning Unfaithful $K$-separable Gaussian Graphical Models: De Wen Soh, Sekhar Tatikonda; (109):1−30, 2019.
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A Representer Theorem for Deep Neural Networks: Michael Unser; (110):1−30, 2019.
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An Efficient Two Step Algorithm for High Dimensional Change Point Regression Models Without Grid Search: Abhishek Kaul, Venkata K. Jandhyala, Stergios B. Fotopoulos; (111):1−40, 2019.
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Measuring the Effects of Data Parallelism on Neural Network Training: Christopher J. Shallue, Jaehoon Lee, Joseph Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl; (112):1−49, 2019.
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Distributed Inference for Linear Support Vector Machine: Xiaozhou Wang, Zhuoyi Yang, Xi Chen, Weidong Liu; (113):1−41, 2019.
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Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery: Richard Y. Zhang, Somayeh Sojoudi, Javad Lavaei; (114):1−34, 2019.
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Learning Attribute Patterns in High-Dimensional Structured Latent Attribute Models: Yuqi Gu, Gongjun Xu; (115):1−58, 2019.
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Graph Reduction with Spectral and Cut Guarantees: Andreas Loukas; (116):1−42, 2019.
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Generic Inference in Latent Gaussian Process Models: Edwin V. Bonilla, Karl Krauth, Amir Dezfouli; (117):1−63, 2019.
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Binarsity: a penalization for one-hot encoded features in linear supervised learning: Mokhtar Z. Alaya, Simon Bussy, Stéphane Gaïffas, Agathe Guilloux; (118):1−34, 2019.
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Layer-Wise Learning Strategy for Nonparametric Tensor Product Smoothing Spline Regression and Graphical Models: Kean Ming Tan, Junwei Lu, Tong Zhang, Han Liu; (119):1−38, 2019.
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Ivanov-Regularised Least-Squares Estimators over Large RKHSs and Their Interpolation Spaces: Stephen Page, Steffen Grünewälder; (120):1−49, 2019.
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Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction: Bin Hong, Weizhong Zhang, Wei Liu, Jieping Ye, Deng Cai, Xiaofei He, Jie Wang; (121):1−39, 2019.
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Approximate Profile Maximum Likelihood: Dmitri S. Pavlichin, Jiantao Jiao, Tsachy Weissman; (122):1−55, 2019.
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ADMMBO: Bayesian Optimization with Unknown Constraints using ADMM: Setareh Ariafar, Jaume Coll-Font, Dana Brooks, Jennifer Dy; (123):1−26, 2019.
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Deep Exploration via Randomized Value Functions: Ian Osband, Benjamin Van Roy, Daniel J. Russo, Zheng Wen; (124):1−62, 2019.
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ORCA: A Matlab/Octave Toolbox for Ordinal Regression: Javier Sánchez-Monedero, Pedro A. Gutiérrez, María Pérez-Ortiz; (125):1−5, 2019. (Machine Learning Open Source Software Paper)
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Learning Representations of Persistence Barcodes: Christoph D. Hofer, Roland Kwitt, Marc Niethammer; (126):1−45, 2019.
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Causal Learning via Manifold Regularization: Steven M. Hill, Chris J. Oates, Duncan A. Blythe, Sach Mukherjee; (127):1−32, 2019.
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Unsupervised Basis Function Adaptation for Reinforcement Learning: Edward Barker, Charl Ras; (128):1−73, 2019.
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Time-to-Event Prediction with Neural Networks and Cox Regression: Håvard Kvamme, Ørnulf Borgan, Ida Scheel; (129):1−30, 2019.
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Logical Explanations for Deep Relational Machines Using Relevance Information: Ashwin Srinivasan, Lovekesh Vig, Michael Bain; (130):1−47, 2019.
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Decoupling Sparsity and Smoothness in the Dirichlet Variational Autoencoder Topic Model: Sophie Burkhardt, Stefan Kramer; (131):1−27, 2019.
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More Efficient Estimation for Logistic Regression with Optimal Subsamples: HaiYing Wang; (132):1−59, 2019.
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Spurious Valleys in One-hidden-layer Neural Network Optimization Landscapes: Luca Venturi, Afonso S. Bandeira, Joan Bruna; (133):1−34, 2019.
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Stochastic Variance-Reduced Cubic Regularization Methods: Dongruo Zhou, Pan Xu, Quanquan Gu; (134):1−47, 2019.
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Gaussian Processes with Linear Operator Inequality Constraints: Christian Agrell; (135):1−36, 2019.
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Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis: Nicolás García Trillos, Daniel Sanz-Alonso, Ruiyi Yang; (136):1−37, 2019.
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Multiclass Boosting: Margins, Codewords, Losses, and Algorithms: Mohammad Saberian, Nuno Vasconcelos; (137):1−68, 2019.
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Generalized Maximum Entropy Estimation: Tobias Sutter, David Sutter, Peyman Mohajerin Esfahani, John Lygeros; (138):1−29, 2019.
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Decentralized Dictionary Learning Over Time-Varying Digraphs: Amir Daneshmand, Ying Sun, Gesualdo Scutari, Francisco Facchinei, Brian M. Sadler; (139):1−62, 2019.
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Nonparametric Bayesian Aggregation for Massive Data: Zuofeng Shang, Botao Hao, Guang Cheng; (140):1−81, 2019.
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Provably Accurate Double-Sparse Coding: Thanh V. Nguyen, Raymond K. W. Wong, Chinmay Hegde; (141):1−43, 2019.
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Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA: Ji Chen, Xiaodong Li; (142):1−39, 2019.
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Minimal Sample Subspace Learning: Theory and Algorithms: Zhenyue Zhang, Yuqing Xia; (143):1−57, 2019.
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Convergence of Gaussian Belief Propagation Under General Pairwise Factorization: Connecting Gaussian MRF with Pairwise Linear Gaussian Model: Bin Li, Yik-Chung Wu; (144):1−30, 2019.
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Bayesian Optimization for Policy Search via Online-Offline Experimentation: Benjamin Letham, Eytan Bakshy; (145):1−30, 2019.
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Characterizing the Sample Complexity of Pure Private Learners: Amos Beimel, Kobbi Nissim, Uri Stemmer; (146):1−33, 2019.
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Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise: Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf; (147):1−50, 2019.
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Collective Matrix Completion: Mokhtar Z. Alaya, Olga Klopp; (148):1−43, 2019.
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On Asymptotic and Finite-Time Optimality of Bayesian Predictors: Daniil Ryabko; (149):1−24, 2019.
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Learning Optimized Risk Scores: Berk Ustun, Cynthia Rudin; (150):1−75, 2019.
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Nonparametric Estimation of Probability Density Functions of Random Persistence Diagrams: Vasileios Maroulas, Joshua L Mike, Christopher Oballe; (151):1−49, 2019.
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High-dimensional Varying Index Coefficient Models via Stein's Identity: Sen Na, Zhuoran Yang, Zhaoran Wang, Mladen Kolar; (152):1−44, 2019.
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Approximation Algorithms for Stochastic Clustering: David G. Harris, Shi Li, Thomas Pensyl, Aravind Srinivasan, Khoa Trinh; (153):1−33, 2019.
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Convergence Guarantees for a Class of Non-convex and Non-smooth Optimization Problems: Koulik Khamaru, Martin J. Wainwright; (154):1−52, 2019.
[abs][pdf][bib]

Quantifying Uncertainty in Online Regression Forests: Theodore Vasiloudis, Gianmarco De Francisci Morales, Henrik Boström; (155):1−35, 2019.
[abs][pdf][bib]

SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition: Yuntao Chen, Chenxia Han, Yanghao Li, Zehao Huang, Yi Jiang, Naiyan Wang, Zhaoxiang Zhang; (156):1−8, 2019.
[abs][pdf][bib] [code]

Simultaneous Phase Retrieval and Blind Deconvolution via Convex Programming: Ali Ahmed, Alireza Aghasi, Paul Hand; (157):1−28, 2019.
[abs][pdf][bib]

GraSPy: Graph Statistics in Python: Jaewon Chung, Benjamin D. Pedigo, Eric W. Bridgeford, Bijan K. Varjavand, Hayden S. Helm, Joshua T. Vogelstein; (158):1−7, 2019.
[abs][pdf][bib] [code]

Optimal Convergence Rates for Convex Distributed Optimization in Networks: Kevin Scaman, Francis Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié; (159):1−31, 2019.
[abs][pdf][bib]

Learning by Unsupervised Nonlinear Diffusion: Mauro Maggioni, James M. Murphy; (160):1−56, 2019.
[abs][pdf][bib]

Sparse Kernel Regression with Coefficient-based $\ell_q-$regularization: Lei Shi, Xiaolin Huang, Yunlong Feng, Johan A.K. Suykens; (161):1−44, 2019.
[abs][pdf][bib]

A Kernel Multiple Change-point Algorithm via Model Selection: Sylvain Arlot, Alain Celisse, Zaid Harchaoui; (162):1−56, 2019.
[abs][pdf][bib]

Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets: Jie Wang, Zhanqiu Zhang, Jieping Ye; (163):1−42, 2019.
[abs][pdf][bib]

The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks: Arjun Sondhi, Ali Shojaie; (164):1−31, 2019.
[abs][pdf][bib]

On the Convergence of Gaussian Belief Propagation with Nodes of Arbitrary Size: Francois Kamper, Sarel J. Steel, Johan A. du Preez; (165):1−37, 2019.
[abs][pdf][bib]

Unsupervised Evaluation and Weighted Aggregation of Ranked Classification Predictions: Mehmet Eren Ahsen, Robert M Vogel, Gustavo A Stolovitzky; (166):1−40, 2019.
[abs][pdf][bib]

Stochastic Canonical Correlation Analysis: Chao Gao, Dan Garber, Nathan Srebro, Jialei Wang, Weiran Wang; (167):1−46, 2019.
[abs][pdf][bib]

Determinantal Point Processes for Coresets: Nicolas Tremblay, Simon Barthelmé, Pierre-Olivier Amblard; (168):1−70, 2019.
[abs][pdf][bib]

Embarrassingly Parallel Inference for Gaussian Processes: Michael Minyi Zhang, Sinead A. Williamson; (169):1−26, 2019.
[abs][pdf][bib]

DBSCAN: Optimal Rates For Density-Based Cluster Estimation: Daren Wang, Xinyang Lu, Alessandro Rinaldo; (170):1−50, 2019.
[abs][pdf][bib]

Shared Subspace Models for Multi-Group Covariance Estimation: Alexander M. Franks, Peter Hoff; (171):1−37, 2019.
[abs][pdf][bib]

Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals: Andrew Cotter, Heinrich Jiang, Maya Gupta, Serena Wang, Taman Narayan, Seungil You, Karthik Sridharan; (172):1−59, 2019.
[abs][pdf][bib]

Fast Automatic Smoothing for Generalized Additive Models: Yousra El-Bachir, Anthony C. Davison; (173):1−27, 2019.
[abs][pdf][bib]

Learning Overcomplete, Low Coherence Dictionaries with Linear Inference: Jesse A. Livezey, Alejandro F. Bujan, Friedrich T. Sommer; (174):1−42, 2019.
[abs][pdf][bib]

DataWig: Missing Value Imputation for Tables: Felix Biessmann, Tammo Rukat, Phillipp Schmidt, Prathik Naidu, Sebastian Schelter, Andrey Taptunov, Dustin Lange, David Salinas; (175):1−6, 2019.
[abs][pdf][bib] [code]

New Convergence Aspects of Stochastic Gradient Algorithms: Lam M. Nguyen, Phuong Ha Nguyen, Peter Richtárik, Katya Scheinberg, Martin Takáč, Marten van Dijk; (176):1−49, 2019.
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All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously: Aaron Fisher, Cynthia Rudin, Francesca Dominici; (177):1−81, 2019.
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Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning: Daniel C. Castro, Jeremy Tan, Bernhard Kainz, Ender Konukoglu, Ben Glocker; (178):1−29, 2019.
[abs][pdf][bib]

Differentiable reservoir computing: Lyudmila Grigoryeva, Juan-Pablo Ortega; (179):1−62, 2019.
[abs][pdf][bib]

DPPy: DPP Sampling with Python: Guillaume Gautier, Guillermo Polito, Rémi Bardenet, Michal Valko; (180):1−7, 2019.
[abs][pdf][bib] [code]

Neural Empirical Bayes: Saeed Saremi, Aapo Hyvärinen; (181):1−23, 2019.
[abs][pdf][bib]

Model Selection in Bayesian Neural Networks via Horseshoe Priors: Soumya Ghosh, Jiayu Yao, Finale Doshi-Velez; (182):1−46, 2019.
[abs][pdf][bib]

Log-concave sampling: Metropolis-Hastings algorithms are fast: Raaz Dwivedi, Yuansi Chen, Martin J. Wainwright, Bin Yu; (183):1−42, 2019.
[abs][pdf][bib]

Why do deep convolutional networks generalize so poorly to small image transformations?: Aharon Azulay, Yair Weiss; (184):1−25, 2019.
[abs][pdf][bib]

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