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JMLR Volume 20

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

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

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

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

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

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.

Forward-Backward Selection with Early Dropping
Giorgos Borboudakis, Ioannis Tsamardinos; (8):1−39, 2019.

Dynamic Pricing in High-dimensions
Adel Javanmard, Hamid Nazerzadeh; (9):1−49, 2019.

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

An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory
Mehmet Eren Ahsen, Mathukumalli Vidyasagar; (11):1−23, 2019.

Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds
Shusen Wang, Alex Gittens, Michael W. Mahoney; (12):1−49, 2019.

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

Approximations of the Restless Bandit Problem
Steffen Grünewälder, Azadeh Khaleghi; (14):1−37, 2019.

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

Smooth neighborhood recommender systems
Ben Dai, Junhui Wang, Xiaotong Shen, Annie Qu; (16):1−24, 2019.

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

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.

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.

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

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

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

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

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

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.

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.

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