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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[code]
- 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 Nystrom 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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[code]
- 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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[code]
- 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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[code]
- Iterated Learning in Dynamic Social Networks
- Bernard Chazelle, Chu Wang; (29):1−28, 2019.
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- Exact Clustering of Weighted Graphs via Semidefinite Programming
- Aleksis Pirinen, Brendan Ames; (30):1−34, 2019.
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- Kernels for Sequentially Ordered Data
- Franz J. Kiraly, Harald Oberhauser; (31):1−45, 2019.
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- NetSDM: Semantic Data Mining with Network Analysis
- Jan Kralj, Marko Robnik-Sikonja, Nada Lavrac; (32):1−50, 2019.
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- 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.
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- 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.
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- Approximation Hardness for A Class of Sparse Optimization Problems
- Yichen Chen, Yinyu Ye, Mengdi Wang; (38):1−27, 2019.
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- 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.
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- DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization
- Lin Xiao, Adams Wei Yu, Qihang Lin, Weizhu Chen; (43):1−58, 2019.
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- 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.
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[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.
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- Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping
- Shao-Bo Lin, Yunwen Lei, Ding-Xuan Zhou; (46):1−36, 2019.
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- Analysis of spectral clustering algorithms for community detection: the general bipartite setting
- Zhixin Zhou, Arash A.Amini; (47):1−47, 2019.
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- 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.
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- 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.
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- Deep Reinforcement Learning for Swarm Systems
- Maximilian Hüttenrauch, Adrian Šošić, Gerhard Neumann; (54):1−31, 2019.
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- 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.
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- The Common-directions Method for Regularized Empirical Risk Minimization
- Po-Wei Wang, Ching-pei Lee, Chih-Jen Lin; (58):1−49, 2019.
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- 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.
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- Robust Estimation of Derivatives Using Locally Weighted Least Absolute Deviation Regression
- WenWu Wang, Ping Yu, Lu Lin, Tiejun Tong; (60):1−49, 2019.
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- 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.
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- 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.
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- 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.
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[code]
- On the optimality of the Hedge algorithm in the stochastic regime
- Jaouad Mourtada, Stéphane Gaïffas; (83):1−28, 2019.
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- Differentiable Game Mechanics
- Alistair Letcher, David Balduzzi, Sébastien Racanière, James Martens, Jakob Foerster, Karl Tuyls, Thore Graepel; (84):1−40, 2019.
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