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.
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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 Nystr\"om 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.
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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.
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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.
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- Iterated Learning in Dynamic Social Networks
- Bernard Chazelle, Chu Wang; (29):1−28, 2019.
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