JMLR Volume 20

• Adaptation Based on Generalized Discrepancy

  Corinna Cortes, Mehryar Mohri, Andrés Muñoz Medina (1):1−30, 2019 PDF BibTeX

• Transport Analysis of Infinitely Deep Neural Network

  Sho Sonoda, Noboru Murata (2):1−52, 2019 PDF BibTeX

• Parsimonious Online Learning with Kernels via Sparse Projections in Function Space

  Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro Ribeiro (3):1−44, 2019 PDF BibTeX

• Convergence Rate of a Simulated Annealing Algorithm with Noisy Observations

  Clément Bouttier, Ioana Gavra (4):1−45, 2019 PDF BibTeX

• Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression

  Han Chen, Garvesh Raskutti, Ming Yuan (5):1−37, 2019 PDF BibTeX

• scikit-multilearn: A Python library for Multi-Label Classification

  Piotr Szymański, Tomasz Kajdanowicz (6):1−22, 2019 codePDF BibTeX

• Scalable Approximations for Generalized Linear Problems

  Murat Erdogdu, Mohsen Bayati, Lee H. Dicker (7):1−45, 2019 PDF BibTeX

• Forward-Backward Selection with Early Dropping

  Giorgos Borboudakis, Ioannis Tsamardinos (8):1−39, 2019 PDF BibTeX

• Dynamic Pricing in High-dimensions

  Adel Javanmard, Hamid Nazerzadeh (9):1−49, 2019 PDF BibTeX

• Graphical Lasso and Thresholding: Equivalence and Closed-form Solutions

  Salar Fattahi, Somayeh Sojoudi (10):1−44, 2019 PDF BibTeX

• An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory

  Mehmet Eren Ahsen, Mathukumalli Vidyasagar (11):1−23, 2019 PDF BibTeX

• Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds

  Shusen Wang, Alex Gittens, Michael W. Mahoney (12):1−49, 2019 PDF BibTeX

• Train and Test Tightness of LP Relaxations in Structured Prediction

  Ofer Meshi, Ben London, Adrian Weller, David Sontag (13):1−34, 2019 PDF BibTeX

• Approximations of the Restless Bandit Problem

  Steffen Grünewälder, Azadeh Khaleghi (14):1−37, 2019 PDF BibTeX

• Automated Scalable Bayesian Inference via Hilbert Coresets

  Trevor Campbell, Tamara Broderick (15):1−38, 2019 PDF BibTeX

• Smooth neighborhood recommender systems

  Ben Dai, Junhui Wang, Xiaotong Shen, Annie Qu (16):1−24, 2019 PDF BibTeX

• Delay and Cooperation in Nonstochastic Bandits

  Nicolò Cesa-Bianchi, Claudio Gentile, Yishay Mansour (17):1−38, 2019 PDF BibTeX

• 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 PDF BibTeX

• spark-crowd: A Spark Package for Learning from Crowdsourced Big Data

  Enrique G. Rodrigo, Juan A. Aledo, José A. Gámez (19):1−5, 2019 codePDF BibTeX

• Accelerated Alternating Projections for Robust Principal Component Analysis

  HanQin Cai, Jian-Feng Cai, Ke Wei (20):1−33, 2019 PDF BibTeX

• Spectrum Estimation from a Few Entries

  Ashish Khetan, Sewoong Oh (21):1−55, 2019 PDF BibTeX

• Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics

  Yanning Shen, Tianyi Chen, Georgios B. Giannakis (22):1−36, 2019 PDF BibTeX

• Determining the Number of Latent Factors in Statistical Multi-Relational Learning

  Chengchun Shi, Wenbin Lu, Rui Song (23):1−38, 2019 PDF BibTeX

• Joint PLDA for Simultaneous Modeling of Two Factors

  Luciana Ferrer, Mitchell McLaren (24):1−29, 2019 PDF BibTeX

• Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations

  Alberto Bietti, Julien Mairal (25):1−49, 2019 PDF BibTeX

• TensorLy: Tensor Learning in Python

  Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic (26):1−6, 2019 codePDF BibTeX

• Monotone Learning with Rectified Wire Networks

  Veit Elser, Dan Schmidt, Jonathan Yedidia (27):1−42, 2019 PDF BibTeX

• 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 codePDF BibTeX

• Iterated Learning in Dynamic Social Networks

  Bernard Chazelle, Chu Wang (29):1−28, 2019 PDF BibTeX

• Exact Clustering of Weighted Graphs via Semidefinite Programming

  Aleksis Pirinen, Brendan Ames (30):1−34, 2019 PDF BibTeX

• Kernels for Sequentially Ordered Data

  Franz J. Kiraly, Harald Oberhauser (31):1−45, 2019 PDF BibTeX

• NetSDM: Semantic Data Mining with Network Analysis

  Jan Kralj, Marko Robnik-Sikonja, Nada Lavrac (32):1−50, 2019 PDF BibTeX

• The Relationship Between Agnostic Selective Classification, Active Learning and the Disagreement Coefficient

  Roei Gelbhart, Ran El-Yaniv (33):1−38, 2019 PDF BibTeX

• Matched Bipartite Block Model with Covariates

  Zahra S. Razaee, Arash A. Amini, Jingyi Jessica Li (34):1−44, 2019 PDF BibTeX

• Optimal Policies for Observing Time Series and Related Restless Bandit Problems

  Christopher R. Dance, Tomi Silander (35):1−93, 2019 PDF BibTeX

• A New Approach to Laplacian Solvers and Flow Problems

  Patrick Rebeschini, Sekhar Tatikonda (36):1−37, 2019 PDF BibTeX

• A Well-Tempered Landscape for Non-convex Robust Subspace Recovery

  Tyler Maunu, Teng Zhang, Gilad Lerman (37):1−59, 2019 PDF BibTeX

• Approximation Hardness for A Class of Sparse Optimization Problems

  Yichen Chen, Yinyu Ye, Mengdi Wang (38):1−27, 2019 PDF BibTeX

• A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication

  Miles E. Lopes, Shusen Wang, Michael W. Mahoney (39):1−40, 2019 PDF BibTeX

• Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations

  Qianxiao Li, Cheng Tai, Weinan E (40):1−47, 2019 PDF BibTeX

• Decontamination of Mutual Contamination Models

  Julian Katz-Samuels, Gilles Blanchard, Clayton Scott (41):1−57, 2019 PDF BibTeX

• Utilizing Second Order Information in Minibatch Stochastic Variance Reduced Proximal Iterations

  Jialei Wang, Tong Zhang (42):1−56, 2019 PDF BibTeX

• DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization

  Lin Xiao, Adams Wei Yu, Qihang Lin, Weizhu Chen (43):1−58, 2019 PDF BibTeX

• 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 codewebpagePDF BibTeX

• Robust Frequent Directions with Application in Online Learning

  Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li, Tong Zhang (45):1−41, 2019 PDF BibTeX

• Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping

  Shao-Bo Lin, Yunwen Lei, Ding-Xuan Zhou (46):1−36, 2019 PDF BibTeX

• Analysis of spectral clustering algorithms for community detection: the general bipartite setting

  Zhixin Zhou, Arash A.Amini (47):1−47, 2019 PDF BibTeX

• 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 PDF BibTeX

• 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 PDF BibTeX

• No-Regret Bayesian Optimization with Unknown Hyperparameters

  Felix Berkenkamp, Angela P. Schoellig, Andreas Krause (50):1−24, 2019 PDF BibTeX

• Bayesian Combination of Probabilistic Classifiers using Multivariate Normal Mixtures

  Gregor Pirš, Erik Štrumbelj (51):1−18, 2019 PDF BibTeX

• 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 PDF BibTeX

• Tunability: Importance of Hyperparameters of Machine Learning Algorithms

  Philipp Probst, Anne-Laure Boulesteix, Bernd Bischl (53):1−32, 2019 PDF BibTeX

• Deep Reinforcement Learning for Swarm Systems

  Maximilian Hüttenrauch, Adrian Šošić, Gerhard Neumann (54):1−31, 2019 PDF BibTeX

• Neural Architecture Search: A Survey

  Thomas Elsken, Jan Hendrik Metzen, Frank Hutter (55):1−21, 2019 PDF BibTeX

• Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices

  Zengfeng Huang (56):1−23, 2019 PDF BibTeX

• Multi-class Heterogeneous Domain Adaptation

  Joey Tianyi Zhou, Ivor W. Tsang, Sinno Jialin Pan, Mingkui Tan (57):1−31, 2019 PDF BibTeX

• The Common-directions Method for Regularized Empirical Risk Minimization

  Po-Wei Wang, Ching-pei Lee, Chih-Jen Lin (58):1−49, 2019 PDF BibTeX

• 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 PDF BibTeX

• Robust Estimation of Derivatives Using Locally Weighted Least Absolute Deviation Regression

  WenWu Wang, Ping Yu, Lu Lin, Tiejun Tong (60):1−49, 2019 PDF BibTeX

• The Sup-norm Perturbation of HOSVD and Low Rank Tensor Denoising

  Dong Xia, Fan Zhou (61):1−42, 2019 PDF BibTeX

• 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 PDF BibTeX

• 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 PDF BibTeX

• A Representer Theorem for Deep Kernel Learning

  Bastian Bohn, Michael Griebel, Christian Rieger (64):1−32, 2019 PDF BibTeX

• Active Learning for Cost-Sensitive Classification

  Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford (65):1−50, 2019 PDF BibTeX

• Proximal Distance Algorithms: Theory and Practice

  Kevin L. Keys, Hua Zhou, Kenneth Lange (66):1−38, 2019 PDF BibTeX

• Learnability of Solutions to Conjunctive Queries

  Hubie Chen, Matthew Valeriote (67):1−28, 2019 PDF BibTeX

• Variance-based Regularization with Convex Objectives

  John Duchi, Hongseok Namkoong (68):1−55, 2019 PDF BibTeX

• On Consistent Vertex Nomination Schemes

  Vince Lyzinski, Keith Levin, Carey E. Priebe (69):1−39, 2019 PDF BibTeX

• Semi-Analytic Resampling in Lasso

  Tomoyuki Obuchi, Yoshiyuki Kabashima (70):1−33, 2019 PDF BibTeX

• Lazifying Conditional Gradient Algorithms

  Gábor Braun, Sebastian Pokutta, Daniel Zink (71):1−42, 2019 PDF BibTeX

• Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning

  Can Karakus, Yifan Sun, Suhas Diggavi, Wotao Yin (72):1−47, 2019 PDF BibTeX

• Analysis of Langevin Monte Carlo via Convex Optimization

  Alain Durmus, Szymon Majewski, Błażej Miasojedow (73):1−46, 2019 PDF BibTeX

• Deep Optimal Stopping

  Sebastian Becker, Patrick Cheridito, Arnulf Jentzen (74):1−25, 2019 PDF BibTeX

• Fairness Constraints: A Flexible Approach for Fair Classification

  Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi (75):1−42, 2019 PDF BibTeX

• Generalized Score Matching for Non-Negative Data

  Shiqing Yu, Mathias Drton, Ali Shojaie (76):1−70, 2019 PDF BibTeX

• Nonuniformity of P-values Can Occur Early in Diverging Dimensions

  Yingying Fan, Emre Demirkaya, Jinchi Lv (77):1−33, 2019 PDF BibTeX

• Prediction Risk for the Horseshoe Regression

  Anindya Bhadra, Jyotishka Datta, Yunfan Li, Nicholas G. Polson, Brandon Willard (78):1−39, 2019 PDF BibTeX

• Quantification Under Prior Probability Shift: the Ratio Estimator and its Extensions

  Afonso Fernandes Vaz, Rafael Izbicki, Rafael Bassi Stern (79):1−33, 2019 PDF BibTeX

• Learning to Match via Inverse Optimal Transport

  Ruilin Li, Xiaojing Ye, Haomin Zhou, Hongyuan Zha (80):1−37, 2019 PDF BibTeX

• Tight Lower Bounds on the VC-dimension of Geometric Set Systems

  Mónika Csikós, Nabil H. Mustafa, Andrey Kupavskii (81):1−8, 2019 PDF BibTeX

• 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 codePDF BibTeX

• On the optimality of the Hedge algorithm in the stochastic regime

  Jaouad Mourtada, Stéphane Gaïffas (83):1−28, 2019 PDF BibTeX

• Differentiable Game Mechanics

  Alistair Letcher, David Balduzzi, Sébastien Racanière, James Martens, Jakob Foerster, Karl Tuyls, Thore Graepel (84):1−40, 2019 PDF BibTeX

• Bayesian Space-Time Partitioning by Sampling and Pruning Spanning Trees

  Leonardo V. Teixeira, Renato M. Assunção, Rosangela H. Loschi (85):1−35, 2019 PDF BibTeX

• Streaming Principal Component Analysis From Incomplete Data

  Armin Eftekhari, Gregory Ongie, Laura Balzano, Michael B. Wakin (86):1−62, 2019 PDF BibTeX

• An asymptotic analysis of distributed nonparametric methods

  Botond Szabó, Harry van Zanten (87):1−30, 2019 PDF BibTeX

• Model Selection via the VC Dimension

  Merlin Mpoudeu, Bertrand Clarke (88):1−26, 2019 PDF BibTeX

• Dependent relevance determination for smooth and structured sparse regression

  Anqi Wu, Oluwasanmi Koyejo, Jonathan Pillow (89):1−43, 2019 PDF BibTeX

• A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization

  Muhammad A Masood, Finale Doshi-Velez (90):1−56, 2019 PDF BibTeX

• Best Arm Identification for Contaminated Bandits

  Jason Altschuler, Victor-Emmanuel Brunel, Alan Malek (91):1−39, 2019 PDF BibTeX

• AffectiveTweets: a Weka Package for Analyzing Affect in Tweets

  Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer, Saif M. Mohammad (92):1−6, 2019 codePDF BibTeX

• 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 codePDF BibTeX

• Simultaneous Private Learning of Multiple Concepts

  Mark Bun, Kobbi Nissim, Uri Stemmer (94):1−34, 2019 PDF BibTeX

• High-Dimensional Poisson Structural Equation Model Learning via $\ell_1$-Regularized Regression

  Gunwoong Park, Sion Park (95):1−41, 2019 PDF BibTeX

• PyOD: A Python Toolbox for Scalable Outlier Detection

  Yue Zhao, Zain Nasrullah, Zheng Li (96):1−7, 2019 codePDF BibTeX

• Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion

  Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou (97):1−22, 2019 PDF BibTeX

• Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data

  Wenjing Liao, Mauro Maggioni (98):1−63, 2019 PDF BibTeX

• Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction

  William Herlands, Daniel B. Neill, Hannes Nickisch, Andrew Gordon Wilson (99):1−51, 2019 PDF BibTeX

• Hamiltonian Monte Carlo with Energy Conserving Subsampling

  Khue-Dung Dang, Matias Quiroz, Robert Kohn, Minh-Ngoc Tran, Mattias Villani (100):1−31, 2019 PDF BibTeX

• Low Permutation-rank Matrices: Structural Properties and Noisy Completion

  Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright (101):1−43, 2019 PDF BibTeX

• 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 PDF BibTeX

• Regularization via Mass Transportation

  Soroosh Shafieezadeh-Abadeh, Daniel Kuhn, Peyman Mohajerin Esfahani (103):1−68, 2019 PDF BibTeX

• Complete Search for Feature Selection in Decision Trees

  Salvatore Ruggieri (104):1−34, 2019 PDF BibTeX

• Optimal Transport: Fast Probabilistic Approximation with Exact Solvers

  Max Sommerfeld, Jörn Schrieber, Yoav Zemel, Axel Munk (105):1−23, 2019 PDF BibTeX

• 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 PDF BibTeX

• Scalable Interpretable Multi-Response Regression via SEED

  Zemin Zheng, M. Taha Bahadori, Yan Liu, Jinchi Lv (107):1−34, 2019 PDF BibTeX

• 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 PDF BibTeX

• Learning Unfaithful $K$-separable Gaussian Graphical Models

  De Wen Soh, Sekhar Tatikonda (109):1−30, 2019 PDF BibTeX

• A Representer Theorem for Deep Neural Networks

  Michael Unser (110):1−30, 2019 PDF BibTeX

• 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 PDF BibTeX

• 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 PDF BibTeX

• Distributed Inference for Linear Support Vector Machine

  Xiaozhou Wang, Zhuoyi Yang, Xi Chen, Weidong Liu (113):1−41, 2019 PDF BibTeX

• 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 PDF BibTeX

• Learning Attribute Patterns in High-Dimensional Structured Latent Attribute Models

  Yuqi Gu, Gongjun Xu (115):1−58, 2019 PDF BibTeX

• Graph Reduction with Spectral and Cut Guarantees

  Andreas Loukas (116):1−42, 2019 PDF BibTeX

• Generic Inference in Latent Gaussian Process Models

  Edwin V. Bonilla, Karl Krauth, Amir Dezfouli (117):1−63, 2019 PDF BibTeX

• 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 PDF BibTeX

• 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 PDF BibTeX

• Ivanov-Regularised Least-Squares Estimators over Large RKHSs and Their Interpolation Spaces

  Stephen Page, Steffen Grünewälder (120):1−49, 2019 PDF BibTeX

• 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 PDF BibTeX

• Approximate Profile Maximum Likelihood

  Dmitri S. Pavlichin, Jiantao Jiao, Tsachy Weissman (122):1−55, 2019 PDF BibTeX

• ADMMBO: Bayesian Optimization with Unknown Constraints using ADMM

  Setareh Ariafar, Jaume Coll-Font, Dana Brooks, Jennifer Dy (123):1−26, 2019 codePDF BibTeX

• Deep Exploration via Randomized Value Functions

  Ian Osband, Benjamin Van Roy, Daniel J. Russo, Zheng Wen (124):1−62, 2019 PDF BibTeX

• ORCA: A Matlab/Octave Toolbox for Ordinal Regression

  Javier Sánchez-Monedero, Pedro A. Gutiérrez, María Pérez-Ortiz (125):1−5, 2019 codePDF BibTeX

• Learning Representations of Persistence Barcodes

  Christoph D. Hofer, Roland Kwitt, Marc Niethammer (126):1−45, 2019 PDF BibTeX

• Causal Learning via Manifold Regularization

  Steven M. Hill, Chris J. Oates, Duncan A. Blythe, Sach Mukherjee (127):1−32, 2019 PDF BibTeX

• Unsupervised Basis Function Adaptation for Reinforcement Learning

  Edward Barker, Charl Ras (128):1−73, 2019 PDF BibTeX

• Time-to-Event Prediction with Neural Networks and Cox Regression

  Håvard Kvamme, Ørnulf Borgan, Ida Scheel (129):1−30, 2019 PDF BibTeX

• Logical Explanations for Deep Relational Machines Using Relevance Information

  Ashwin Srinivasan, Lovekesh Vig, Michael Bain (130):1−47, 2019 PDF BibTeX

• Decoupling Sparsity and Smoothness in the Dirichlet Variational Autoencoder Topic Model

  Sophie Burkhardt, Stefan Kramer (131):1−27, 2019 PDF BibTeX

• More Efficient Estimation for Logistic Regression with Optimal Subsamples

  HaiYing Wang (132):1−59, 2019 PDF BibTeX

• Spurious Valleys in One-hidden-layer Neural Network Optimization Landscapes

  Luca Venturi, Afonso S. Bandeira, Joan Bruna (133):1−34, 2019 PDF BibTeX

• Stochastic Variance-Reduced Cubic Regularization Methods

  Dongruo Zhou, Pan Xu, Quanquan Gu (134):1−47, 2019 PDF BibTeX

• Gaussian Processes with Linear Operator Inequality Constraints

  Christian Agrell (135):1−36, 2019 PDF BibTeX

• 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 PDF BibTeX

• Multiclass Boosting: Margins, Codewords, Losses, and Algorithms

  Mohammad Saberian, Nuno Vasconcelos (137):1−68, 2019 PDF BibTeX

• Generalized Maximum Entropy Estimation

  Tobias Sutter, David Sutter, Peyman Mohajerin Esfahani, John Lygeros (138):1−29, 2019 PDF BibTeX

• Decentralized Dictionary Learning Over Time-Varying Digraphs

  Amir Daneshmand, Ying Sun, Gesualdo Scutari, Francisco Facchinei, Brian M. Sadler (139):1−62, 2019 PDF BibTeX

• Nonparametric Bayesian Aggregation for Massive Data

  Zuofeng Shang, Botao Hao, Guang Cheng (140):1−81, 2019 PDF BibTeX

• Provably Accurate Double-Sparse Coding

  Thanh V. Nguyen, Raymond K. W. Wong, Chinmay Hegde (141):1−43, 2019 PDF BibTeX

• Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA

  Ji Chen, Xiaodong Li (142):1−39, 2019 PDF BibTeX

• Minimal Sample Subspace Learning: Theory and Algorithms

  Zhenyue Zhang, Yuqing Xia (143):1−57, 2019 PDF BibTeX

• 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 PDF BibTeX

• Bayesian Optimization for Policy Search via Online-Offline Experimentation

  Benjamin Letham, Eytan Bakshy (145):1−30, 2019 appendixPDF BibTeX

• Characterizing the Sample Complexity of Pure Private Learners

  Amos Beimel, Kobbi Nissim, Uri Stemmer (146):1−33, 2019 PDF BibTeX

• Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise

  Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf (147):1−50, 2019 codePDF BibTeX

• Collective Matrix Completion

  Mokhtar Z. Alaya, Olga Klopp (148):1−43, 2019 PDF BibTeX

• On Asymptotic and Finite-Time Optimality of Bayesian Predictors

  Daniil Ryabko (149):1−24, 2019 PDF BibTeX

• Learning Optimized Risk Scores

  Berk Ustun, Cynthia Rudin (150):1−75, 2019 PDF BibTeX

• Nonparametric Estimation of Probability Density Functions of Random Persistence Diagrams

  Vasileios Maroulas, Joshua L Mike, Christopher Oballe (151):1−49, 2019 PDF BibTeX

• High-dimensional Varying Index Coefficient Models via Stein's Identity

  Sen Na, Zhuoran Yang, Zhaoran Wang, Mladen Kolar (152):1−44, 2019 PDF BibTeX

• Approximation Algorithms for Stochastic Clustering

  David G. Harris, Shi Li, Thomas Pensyl, Aravind Srinivasan, Khoa Trinh (153):1−33, 2019 PDF BibTeX

• Convergence Guarantees for a Class of Non-convex and Non-smooth Optimization Problems

  Koulik Khamaru, Martin J. Wainwright (154):1−52, 2019 PDF BibTeX

• Quantifying Uncertainty in Online Regression Forests

  Theodore Vasiloudis, Gianmarco De Francisci Morales, Henrik Boström (155):1−35, 2019 PDF BibTeX

• 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 codePDF BibTeX

• Simultaneous Phase Retrieval and Blind Deconvolution via Convex Programming

  Ali Ahmed, Alireza Aghasi, Paul Hand (157):1−28, 2019 PDF BibTeX

• 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 codePDF BibTeX

• Optimal Convergence Rates for Convex Distributed Optimization in Networks

  Kevin Scaman, Francis Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié (159):1−31, 2019 PDF BibTeX

• Learning by Unsupervised Nonlinear Diffusion

  Mauro Maggioni, James M. Murphy (160):1−56, 2019 PDF BibTeX

• Sparse Kernel Regression with Coefficient-based $\ell_q-$regularization

  Lei Shi, Xiaolin Huang, Yunlong Feng, Johan A.K. Suykens (161):1−44, 2019 PDF BibTeX

• A Kernel Multiple Change-point Algorithm via Model Selection

  Sylvain Arlot, Alain Celisse, Zaid Harchaoui (162):1−56, 2019 PDF BibTeX

• Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets

  Jie Wang, Zhanqiu Zhang, Jieping Ye (163):1−42, 2019 PDF BibTeX

• The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks

  Arjun Sondhi, Ali Shojaie (164):1−31, 2019 PDF BibTeX

• 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 PDF BibTeX

• Unsupervised Evaluation and Weighted Aggregation of Ranked Classification Predictions

  Mehmet Eren Ahsen, Robert M Vogel, Gustavo A Stolovitzky (166):1−40, 2019 PDF BibTeX

• Stochastic Canonical Correlation Analysis

  Chao Gao, Dan Garber, Nathan Srebro, Jialei Wang, Weiran Wang (167):1−46, 2019 PDF BibTeX

• Determinantal Point Processes for Coresets

  Nicolas Tremblay, Simon Barthelmé, Pierre-Olivier Amblard (168):1−70, 2019 PDF BibTeX

• Embarrassingly Parallel Inference for Gaussian Processes

  Michael Minyi Zhang, Sinead A. Williamson (169):1−26, 2019 PDF BibTeX

• DBSCAN: Optimal Rates For Density-Based Cluster Estimation

  Daren Wang, Xinyang Lu, Alessandro Rinaldo (170):1−50, 2019 PDF BibTeX

• Shared Subspace Models for Multi-Group Covariance Estimation

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