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JMLR Volume 20

Adaptation Based on Generalized Discrepancy
Corinna Cortes, Mehryar Mohri, Andrés Muñoz Medina; (1):1−30, 2019.
[abs][pdf][bib]

Transport Analysis of Infinitely Deep Neural Network
Sho Sonoda, Noboru Murata; (2):1−52, 2019.
[abs][pdf][bib]

Parsimonious Online Learning with Kernels via Sparse Projections in Function Space
Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro Ribeiro; (3):1−44, 2019.
[abs][pdf][bib]

Convergence Rate of a Simulated Annealing Algorithm with Noisy Observations
Clément Bouttier, Ioana Gavra; (4):1−45, 2019.
[abs][pdf][bib]

Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression
Han Chen, Garvesh Raskutti, Ming Yuan; (5):1−37, 2019.
[abs][pdf][bib]

scikit-multilearn: A Python library for Multi-Label Classification
Piotr Szymański, Tomasz Kajdanowicz; (6):1−22, 2019.
[abs][pdf][bib]      [code]

Scalable Approximations for Generalized Linear Problems
Murat Erdogdu, Mohsen Bayati, Lee H. Dicker; (7):1−45, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Graphical Lasso and Thresholding: Equivalence and Closed-form Solutions
Salar Fattahi, Somayeh Sojoudi; (10):1−44, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Train and Test Tightness of LP Relaxations in Structured Prediction
Ofer Meshi, Ben London, Adrian Weller, David Sontag; (13):1−34, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Delay and Cooperation in Nonstochastic Bandits
Nicolò Cesa-Bianchi, Claudio Gentile, Yishay Mansour; (17):1−38, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

spark-crowd: A Spark Package for Learning from Crowdsourced Big Data
Enrique G. Rodrigo, Juan A. Aledo, José A. Gámez; (19):1−5, 2019.
[abs][pdf][bib]      [code]

Accelerated Alternating Projections for Robust Principal Component Analysis
HanQin Cai, Jian-Feng Cai, Ke Wei; (20):1−33, 2019.
[abs][pdf][bib]

Spectrum Estimation from a Few Entries
Ashish Khetan, Sewoong Oh; (21):1−55, 2019.
[abs][pdf][bib]

Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics
Yanning Shen, Tianyi Chen, Georgios B. Giannakis; (22):1−36, 2019.
[abs][pdf][bib]

Determining the Number of Latent Factors in Statistical Multi-Relational Learning
Chengchun Shi, Wenbin Lu, Rui Song; (23):1−38, 2019.
[abs][pdf][bib]

Joint PLDA for Simultaneous Modeling of Two Factors
Luciana Ferrer, Mitchell McLaren; (24):1−29, 2019.
[abs][pdf][bib]

Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations
Alberto Bietti, Julien Mairal; (25):1−49, 2019.
[abs][pdf][bib]

TensorLy: Tensor Learning in Python
Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic; (26):1−6, 2019.
[abs][pdf][bib]      [code]

Monotone Learning with Rectified Wire Networks
Veit Elser, Dan Schmidt, Jonathan Yedidia; (27):1−42, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]      [code]

Iterated Learning in Dynamic Social Networks
Bernard Chazelle, Chu Wang; (29):1−28, 2019.
[abs][pdf][bib]

Exact Clustering of Weighted Graphs via Semidefinite Programming
Aleksis Pirinen, Brendan Ames; (30):1−34, 2019.
[abs][pdf][bib]

Kernels for Sequentially Ordered Data
Franz J. Kiraly, Harald Oberhauser; (31):1−45, 2019.
[abs][pdf][bib]

NetSDM: Semantic Data Mining with Network Analysis
Jan Kralj, Marko Robnik-Sikonja, Nada Lavrac; (32):1−50, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Approximation Hardness for A Class of Sparse Optimization Problems
Yichen Chen, Yinyu Ye, Mengdi Wang; (38):1−27, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization
Lin Xiao, Adams Wei Yu, Qihang Lin, Weizhu Chen; (43):1−58, 2019.
[abs][pdf][bib]

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

Robust Frequent Directions with Application in Online Learning
Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li, Tong Zhang; (45):1−41, 2019.
[abs][pdf][bib]

Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping
Shao-Bo Lin, Yunwen Lei, Ding-Xuan Zhou; (46):1−36, 2019.
[abs][pdf][bib]

Analysis of spectral clustering algorithms for community detection: the general bipartite setting
Zhixin Zhou, Arash A.Amini; (47):1−47, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Deep Reinforcement Learning for Swarm Systems
Maximilian Hüttenrauch, Adrian Šošić, Gerhard Neumann; (54):1−31, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

The Common-directions Method for Regularized Empirical Risk Minimization
Po-Wei Wang, Ching-pei Lee, Chih-Jen Lin; (58):1−49, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Robust Estimation of Derivatives Using Locally Weighted Least Absolute Deviation Regression
WenWu Wang, Ping Yu, Lu Lin, Tiejun Tong; (60):1−49, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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.
[abs][pdf][bib]      [code]

On the optimality of the Hedge algorithm in the stochastic regime
Jaouad Mourtada, Stéphane Gaïffas; (83):1−28, 2019.
[abs][pdf][bib]

Differentiable Game Mechanics
Alistair Letcher, David Balduzzi, Sébastien Racanière, James Martens, Jakob Foerster, Karl Tuyls, Thore Graepel; (84):1−40, 2019.
[abs][pdf][bib]

Bayesian Space-Time Partitioning by Sampling and Pruning Spanning Trees
Leonardo V. Teixeira, Renato M. Assunção, Rosangela H. Loschi; (85):1−35, 2019.
[abs][pdf][bib]

Streaming Principal Component Analysis From Incomplete Data
Armin Eftekhari, Gregory Ongie, Laura Balzano, Michael B. Wakin; (86):1−62, 2019.
[abs][pdf][bib]

An asymptotic analysis of distributed nonparametric methods
Botond Szabó, Harry van Zanten; (87):1−30, 2019.
[abs][pdf][bib]

Model Selection via the VC Dimension
Merlin Mpoudeu, Bertrand Clarke; (88):1−26, 2019.
[abs][pdf][bib]

Dependent relevance determination for smooth and structured sparse regression
Anqi Wu, Oluwasanmi Koyejo, Jonathan Pillow; (89):1−43, 2019.
[abs][pdf][bib]

A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization
Muhammad A Masood, Finale Doshi-Velez; (90):1−56, 2019.
[abs][pdf][bib]

Best Arm Identification for Contaminated Bandits
Jason Altschuler, Victor-Emmanuel Brunel, Alan Malek; (91):1−39, 2019.
[abs][pdf][bib]

AffectiveTweets: a Weka Package for Analyzing Affect in Tweets
Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer, Saif M. Mohammad; (92):1−6, 2019.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

Simultaneous Private Learning of Multiple Concepts
Mark Bun, Kobbi Nissim, Uri Stemmer; (94):1−34, 2019.
[abs][pdf][bib]

High-Dimensional Poisson Structural Equation Model Learning via $\ell_1$-Regularized Regression
Gunwoong Park, Sion Park; (95):1−41, 2019.
[abs][pdf][bib]

PyOD: A Python Toolbox for Scalable Outlier Detection
Yue Zhao, Zain Nasrullah, Zheng Li; (96):1−7, 2019.
[abs][pdf][bib]      [code]

Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion
Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou; (97):1−22, 2019.
[abs][pdf][bib]

Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data
Wenjing Liao, Mauro Maggioni; (98):1−63, 2019.
[abs][pdf][bib]

Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction
William Herlands, Daniel B. Neill, Hannes Nickisch, Andrew Gordon Wilson; (99):1−51, 2019.
[abs][pdf][bib]

Hamiltonian Monte Carlo with Energy Conserving Subsampling
Khue-Dung Dang, Matias Quiroz, Robert Kohn, Minh-Ngoc Tran, Mattias Villani; (100):1−31, 2019.
[abs][pdf][bib]

Low Permutation-rank Matrices: Structural Properties and Noisy Completion
Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright; (101):1−43, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Regularization via Mass Transportation
Soroosh Shafieezadeh-Abadeh, Daniel Kuhn, Peyman Mohajerin Esfahani; (103):1−68, 2019.
[abs][pdf][bib]

Complete Search for Feature Selection in Decision Trees
Salvatore Ruggieri; (104):1−34, 2019.
[abs][pdf][bib]

Optimal Transport: Fast Probabilistic Approximation with Exact Solvers
Max Sommerfeld, Jörn Schrieber, Yoav Zemel, Axel Munk; (105):1−23, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Scalable Interpretable Multi-Response Regression via SEED
Zemin Zheng, M. Taha Bahadori, Yan Liu, Jinchi Lv; (107):1−34, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Learning Unfaithful $K$-separable Gaussian Graphical Models
De Wen Soh, Sekhar Tatikonda; (109):1−30, 2019.
[abs][pdf][bib]

A Representer Theorem for Deep Neural Networks
Michael Unser; (110):1−30, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Distributed Inference for Linear Support Vector Machine
Xiaozhou Wang, Zhuoyi Yang, Xi Chen, Weidong Liu; (113):1−41, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Learning Attribute Patterns in High-Dimensional Structured Latent Attribute Models
Yuqi Gu, Gongjun Xu; (115):1−58, 2019.
[abs][pdf][bib]

Graph Reduction with Spectral and Cut Guarantees
Andreas Loukas; (116):1−42, 2019.
[abs][pdf][bib]

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