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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. (Machine Learning Open Source Software Paper)
[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.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Automated Scalable Bayesian Inference via Hilbert Coresets
Trevor Campbell, Tamara Broderick; (15):1−38, 2019.
[abs][pdf][bib]

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. (Machine Learning Open Source Software Paper)
[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. (Machine Learning Open Source Software Paper)
[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. (Machine Learning Open Source Software Paper)
[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.
[abs][pdf][bib]

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.
[abs][pdf][bib]

A New Approach to Laplacian Solvers and Flow Problems
Patrick Rebeschini, Sekhar Tatikonda; (36):1−37, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations
Qianxiao Li, Cheng Tai, Weinan E; (40):1−47, 2019.
[abs][pdf][bib]

Decontamination of Mutual Contamination Models
Julian Katz-Samuels, Gilles Blanchard, Clayton Scott; (41):1−57, 2019.
[abs][pdf][bib]

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. (Machine Learning Open Source Software Paper)
[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.
[abs][pdf][bib]

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.
[abs][pdf][bib]

No-Regret Bayesian Optimization with Unknown Hyperparameters
Felix Berkenkamp, Angela P. Schoellig, Andreas Krause; (50):1−24, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices
Zengfeng Huang; (56):1−23, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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.
[abs][pdf][bib]

A Representer Theorem for Deep Kernel Learning
Bastian Bohn, Michael Griebel, Christian Rieger; (64):1−32, 2019.
[abs][pdf][bib]

Active Learning for Cost-Sensitive Classification
Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford; (65):1−50, 2019.
[abs][pdf][bib]

Proximal Distance Algorithms: Theory and Practice
Kevin L. Keys, Hua Zhou, Kenneth Lange; (66):1−38, 2019.
[abs][pdf][bib]

Learnability of Solutions to Conjunctive Queries
Hubie Chen, Matthew Valeriote; (67):1−28, 2019.
[abs][pdf][bib]

Variance-based Regularization with Convex Objectives
John Duchi, Hongseok Namkoong; (68):1−55, 2019.
[abs][pdf][bib]

On Consistent Vertex Nomination Schemes
Vince Lyzinski, Keith Levin, Carey E. Priebe; (69):1−39, 2019.
[abs][pdf][bib]

Semi-Analytic Resampling in Lasso
Tomoyuki Obuchi, Yoshiyuki Kabashima; (70):1−33, 2019.
[abs][pdf][bib]

Lazifying Conditional Gradient Algorithms
Gábor Braun, Sebastian Pokutta, Daniel Zink; (71):1−42, 2019.
[abs][pdf][bib]

Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning
Can Karakus, Yifan Sun, Suhas Diggavi, Wotao Yin; (72):1−47, 2019.
[abs][pdf][bib]

Analysis of Langevin Monte Carlo via Convex Optimization
Alain Durmus, Szymon Majewski, Błażej Miasojedow; (73):1−46, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Generalized Score Matching for Non-Negative Data
Shiqing Yu, Mathias Drton, Ali Shojaie; (76):1−70, 2019.
[abs][pdf][bib]

Nonuniformity of P-values Can Occur Early in Diverging Dimensions
Yingying Fan, Emre Demirkaya, Jinchi Lv; (77):1−33, 2019.
[abs][pdf][bib]

Prediction Risk for the Horseshoe Regression
Anindya Bhadra, Jyotishka Datta, Yunfan Li, Nicholas G. Polson, Brandon Willard; (78):1−39, 2019.
[abs][pdf][bib]

Quantification Under Prior Probability Shift: the Ratio Estimator and its Extensions
Afonso Fernandes Vaz, Rafael Izbicki, Rafael Bassi Stern; (79):1−33, 2019.
[abs][pdf][bib]

Learning to Match via Inverse Optimal Transport
Ruilin Li, Xiaojing Ye, Haomin Zhou, Hongyuan Zha; (80):1−37, 2019.
[abs][pdf][bib]

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. (Machine Learning Open Source Software Paper)
[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. (Machine Learning Open Source Software Paper)
[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. (Machine Learning Open Source Software Paper)
[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. (Machine Learning Open Source Software Paper)
[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]

Generic Inference in Latent Gaussian Process Models
Edwin V. Bonilla, Karl Krauth, Amir Dezfouli; (117):1−63, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Ivanov-Regularised Least-Squares Estimators over Large RKHSs and Their Interpolation Spaces
Stephen Page, Steffen Grünewälder; (120):1−49, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Approximate Profile Maximum Likelihood
Dmitri S. Pavlichin, Jiantao Jiao, Tsachy Weissman; (122):1−55, 2019.
[abs][pdf][bib]

ADMMBO: Bayesian Optimization with Unknown Constraints using ADMM
Setareh Ariafar, Jaume Coll-Font, Dana Brooks, Jennifer Dy; (123):1−26, 2019.
[abs][pdf][bib]      [code]

Deep Exploration via Randomized Value Functions
Ian Osband, Benjamin Van Roy, Daniel J. Russo, Zheng Wen; (124):1−62, 2019.
[abs][pdf][bib]

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)
[abs][pdf][bib]      [code]

Learning Representations of Persistence Barcodes
Christoph D. Hofer, Roland Kwitt, Marc Niethammer; (126):1−45, 2019.
[abs][pdf][bib]

Causal Learning via Manifold Regularization
Steven M. Hill, Chris J. Oates, Duncan A. Blythe, Sach Mukherjee; (127):1−32, 2019.
[abs][pdf][bib]

Unsupervised Basis Function Adaptation for Reinforcement Learning
Edward Barker, Charl Ras; (128):1−73, 2019.
[abs][pdf][bib]

Time-to-Event Prediction with Neural Networks and Cox Regression
Håvard Kvamme, Ørnulf Borgan, Ida Scheel; (129):1−30, 2019.
[abs][pdf][bib]

Logical Explanations for Deep Relational Machines Using Relevance Information
Ashwin Srinivasan, Lovekesh Vig, Michael Bain; (130):1−47, 2019.
[abs][pdf][bib]

Decoupling Sparsity and Smoothness in the Dirichlet Variational Autoencoder Topic Model
Sophie Burkhardt, Stefan Kramer; (131):1−27, 2019.
[abs][pdf][bib]

More Efficient Estimation for Logistic Regression with Optimal Subsamples
HaiYing Wang; (132):1−59, 2019.
[abs][pdf][bib]

Spurious Valleys in One-hidden-layer Neural Network Optimization Landscapes
Luca Venturi, Afonso S. Bandeira, Joan Bruna; (133):1−34, 2019.
[abs][pdf][bib]

Stochastic Variance-Reduced Cubic Regularization Methods
Dongruo Zhou, Pan Xu, Quanquan Gu; (134):1−47, 2019.
[abs][pdf][bib]

Gaussian Processes with Linear Operator Inequality Constraints
Christian Agrell; (135):1−36, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Multiclass Boosting: Margins, Codewords, Losses, and Algorithms
Mohammad Saberian, Nuno Vasconcelos; (137):1−68, 2019.
[abs][pdf][bib]

Generalized Maximum Entropy Estimation
Tobias Sutter, David Sutter, Peyman Mohajerin Esfahani, John Lygeros; (138):1−29, 2019.
[abs][pdf][bib]

Decentralized Dictionary Learning Over Time-Varying Digraphs
Amir Daneshmand, Ying Sun, Gesualdo Scutari, Francisco Facchinei, Brian M. Sadler; (139):1−62, 2019.
[abs][pdf][bib]

Nonparametric Bayesian Aggregation for Massive Data
Zuofeng Shang, Botao Hao, Guang Cheng; (140):1−81, 2019.
[abs][pdf][bib]

Provably Accurate Double-Sparse Coding
Thanh V. Nguyen, Raymond K. W. Wong, Chinmay Hegde; (141):1−43, 2019.
[abs][pdf][bib]

Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA
Ji Chen, Xiaodong Li; (142):1−39, 2019.
[abs][pdf][bib]

Minimal Sample Subspace Learning: Theory and Algorithms
Zhenyue Zhang, Yuqing Xia; (143):1−57, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Bayesian Optimization for Policy Search via Online-Offline Experimentation
Benjamin Letham, Eytan Bakshy; (145):1−30, 2019.
[abs][pdf][bib]      [appendix]

Characterizing the Sample Complexity of Pure Private Learners
Amos Beimel, Kobbi Nissim, Uri Stemmer; (146):1−33, 2019.
[abs][pdf][bib]

Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise
Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf; (147):1−50, 2019.
[abs][pdf][bib]      [code]

Collective Matrix Completion
Mokhtar Z. Alaya, Olga Klopp; (148):1−43, 2019.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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.
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Quantifying Uncertainty in Online Regression Forests
Theodore Vasiloudis, Gianmarco De Francisci Morales, Henrik Boström; (155):1−35, 2019.
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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.
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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.
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Optimal Convergence Rates for Convex Distributed Optimization in Networks
Kevin Scaman, Francis Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié; (159):1−31, 2019.
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Learning by Unsupervised Nonlinear Diffusion
Mauro Maggioni, James M. Murphy; (160):1−56, 2019.
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Sparse Kernel Regression with Coefficient-based $\ell_q-$regularization
Lei Shi, Xiaolin Huang, Yunlong Feng, Johan A.K. Suykens; (161):1−44, 2019.
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A Kernel Multiple Change-point Algorithm via Model Selection
Sylvain Arlot, Alain Celisse, Zaid Harchaoui; (162):1−56, 2019.
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Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets
Jie Wang, Zhanqiu Zhang, Jieping Ye; (163):1−42, 2019.
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The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks
Arjun Sondhi, Ali Shojaie; (164):1−31, 2019.
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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.
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Unsupervised Evaluation and Weighted Aggregation of Ranked Classification Predictions
Mehmet Eren Ahsen, Robert M Vogel, Gustavo A Stolovitzky; (166):1−40, 2019.
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Stochastic Canonical Correlation Analysis
Chao Gao, Dan Garber, Nathan Srebro, Jialei Wang, Weiran Wang; (167):1−46, 2019.
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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.
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Shared Subspace Models for Multi-Group Covariance Estimation
Alexander M. Franks, Peter Hoff; (171):1−37, 2019.
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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.
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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.
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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.
[abs][pdf][bib]

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.
[abs][pdf][bib]

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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