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.
[abs][pdf][bib]
- 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.
[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)
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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. (Machine Learning Open Source Software Paper)
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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. (Machine Learning Open Source Software Paper)
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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. (Machine Learning Open Source Software Paper)
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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. (Machine Learning Open Source Software Paper)
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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.
[abs][pdf][bib]
- 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.
[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)
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[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.
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- Bayesian Space-Time Partitioning by Sampling and Pruning Spanning Trees
- Leonardo V. Teixeira, Renato M. Assunção, Rosangela H. Loschi; (85):1−35, 2019.
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- Streaming Principal Component Analysis From Incomplete Data
- Armin Eftekhari, Gregory Ongie, Laura Balzano, Michael B. Wakin; (86):1−62, 2019.
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- An asymptotic analysis of distributed nonparametric methods
- Botond Szabó, Harry van Zanten; (87):1−30, 2019.
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- Model Selection via the VC Dimension
- Merlin Mpoudeu, Bertrand Clarke; (88):1−26, 2019.
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- 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)
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[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)
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[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)
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[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.
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- 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
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- Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise
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[code]
- Collective Matrix Completion
- Mokhtar Z. Alaya, Olga Klopp; (148):1−43, 2019.
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- On Asymptotic and Finite-Time Optimality of Bayesian Predictors
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- Learning Optimized Risk Scores
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- Nonparametric Estimation of Probability Density Functions of Random Persistence Diagrams
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- High-dimensional Varying Index Coefficient Models via Stein's Identity
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- Approximation Algorithms for Stochastic Clustering
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- Quantifying Uncertainty in Online Regression Forests
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[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
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[code]
- Optimal Convergence Rates for Convex Distributed Optimization in Networks
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- Learning by Unsupervised Nonlinear Diffusion
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- Sparse Kernel Regression with Coefficient-based $\ell_q-$regularization
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- A Kernel Multiple Change-point Algorithm via Model Selection
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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
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- Unsupervised Evaluation and Weighted Aggregation of Ranked Classification Predictions
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- Stochastic Canonical Correlation Analysis
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- Determinantal Point Processes for Coresets
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- Embarrassingly Parallel Inference for Gaussian Processes
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- DBSCAN: Optimal Rates For Density-Based Cluster Estimation
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- Shared Subspace Models for Multi-Group Covariance Estimation
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- Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
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- Fast Automatic Smoothing for Generalized Additive Models
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- Learning Overcomplete, Low Coherence Dictionaries with Linear Inference
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- DataWig: Missing Value Imputation for Tables
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[code]
- New Convergence Aspects of Stochastic Gradient Algorithms
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- All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
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- Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning
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- Differentiable reservoir computing
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- DPPy: DPP Sampling with Python
- Guillaume Gautier, Guillermo Polito, Rémi Bardenet, Michal Valko; (180):1−7, 2019.
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[code]
- Neural Empirical Bayes
- Saeed Saremi, Aapo Hyvärinen; (181):1−23, 2019.
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- Model Selection in Bayesian Neural Networks via Horseshoe Priors
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- Log-concave sampling: Metropolis-Hastings algorithms are fast
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- Why do deep convolutional networks generalize so poorly to small image transformations?
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