ML Anthology

JMLR 2019

174 papers

A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication Miles E. Lopes, Shusen Wang, Michael W. Mahoney
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A Kernel Multiple Change-Point Algorithm via Model Selection Sylvain Arlot, Alain Celisse, Zaid Harchaoui
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A New Approach to Laplacian Solvers and Flow Problems Patrick Rebeschini, Sekhar Tatikonda
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A Particle-Based Variational Approach to Bayesian Non-Negative Matrix Factorization Muhammad A Masood, Finale Doshi-Velez
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A Representer Theorem for Deep Kernel Learning Bastian Bohn, Michael Griebel, Christian Rieger
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A Representer Theorem for Deep Neural Networks Michael Unser
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A Well-Tempered Landscape for Non-Convex Robust Subspace Recovery Tyler Maunu, Teng Zhang, Gilad Lerman
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Accelerated Alternating Projections for Robust Principal Component Analysis HanQin Cai, Jian-Feng Cai, Ke Wei
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Active Learning for Cost-Sensitive Classification Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé Iii, John Langford
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Adaptation Based on Generalized Discrepancy Corinna Cortes, Mehryar Mohri, Andrés Muñoz Medina
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Adaptive Geometric Multiscale Approximations for Intrinsically Low-Dimensional Data Wenjing Liao, Mauro Maggioni
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ADMMBO: Bayesian Optimization with Unknown Constraints Using ADMM Setareh Ariafar, Jaume Coll-Font, Dana Brooks, Jennifer Dy
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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 Aaron Fisher, Cynthia Rudin, Francesca Dominici
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An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory Mehmet Eren Ahsen, Mathukumalli Vidyasagar
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An Asymptotic Analysis of Distributed Nonparametric Methods Botond Szabó, Harry van Zanten
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An Efficient Two Step Algorithm for High Dimensional Change Point Regression Models Without Grid Search Abhishek Kaul, Venkata K. Jandhyala, Stergios B. Fotopoulos
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Analysis of Langevin Monte Carlo via Convex Optimization Alain Durmus, Szymon Majewski, Błażej Miasojedow
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Analysis of Spectral Clustering Algorithms for Community Detection: The General Bipartite Setting Zhixin Zhou, Arash A.Amini
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Approximate Profile Maximum Likelihood Dmitri S. Pavlichin, Jiantao Jiao, Tsachy Weissman
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Approximation Algorithms for Stochastic Clustering David G. Harris, Shi Li, Thomas Pensyl, Aravind Srinivasan, Khoa Trinh
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Approximation Hardness for a Class of Sparse Optimization Problems Yichen Chen, Yinyu Ye, Mengdi Wang
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Approximations of the Restless Bandit Problem Steffen Grünewälder, Azadeh Khaleghi
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Automated Scalable Bayesian Inference via Hilbert Coresets Trevor Campbell, Tamara Broderick
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Bayesian Combination of Probabilistic Classifiers Using Multivariate Normal Mixtures Gregor Pirš, Erik Štrumbelj
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Bayesian Optimization for Policy Search via Online-Offline Experimentation Benjamin Letham, Eytan Bakshy
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Bayesian Space-Time Partitioning by Sampling and Pruning Spanning Trees Leonardo V. Teixeira, Renato M. Assunção, Rosangela H. Loschi
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Best Arm Identification for Contaminated Bandits Jason Altschuler, Victor-Emmanuel Brunel, Alan Malek
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Binarsity: A Penalization for One-Hot Encoded Features in Linear Supervised Learning Mokhtar Z. Alaya, Simon Bussy, Stéphane Gaïffas, Agathe Guilloux
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Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping Shao-Bo Lin, Yunwen Lei, Ding-Xuan Zhou
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Causal Learning via Manifold Regularization Steven M. Hill, Chris J. Oates, Duncan A. Blythe, Sach Mukherjee
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Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction William Herlands, Daniel B. Neill, Hannes Nickisch, Andrew Gordon Wilson
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Characterizing the Sample Complexity of Pure Private Learners Amos Beimel, Kobbi Nissim, Uri Stemmer
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Collective Matrix Completion Mokhtar Z. Alaya, Olga Klopp
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Complete Search for Feature Selection in Decision Trees Salvatore Ruggieri
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Convergence Guarantees for a Class of Non-Convex and Non-Smooth Optimization Problems Koulik Khamaru, Martin J. Wainwright
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Convergence of Gaussian Belief Propagation Under General Pairwise Factorization: Connecting Gaussian MRF with Pairwise Linear Gaussian Model Bin Li, Yik-Chung Wu
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Convergence Rate of a Simulated Annealing Algorithm with Noisy Observations Clément Bouttier, Ioana Gavra
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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
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DBSCAN: Optimal Rates for Density-Based Cluster Estimation Daren Wang, Xinyang Lu, Alessandro Rinaldo
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Decentralized Dictionary Learning over Time-Varying Digraphs Amir Daneshmand, Ying Sun, Gesualdo Scutari, Francisco Facchinei, Brian M. Sadler
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Decontamination of Mutual Contamination Models Julian Katz-Samuels, Gilles Blanchard, Clayton Scott
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Decoupling Sparsity and Smoothness in the Dirichlet Variational Autoencoder Topic Model Sophie Burkhardt, Stefan Kramer
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Deep Exploration via Randomized Value Functions Ian Osband, Benjamin Van Roy, Daniel J. Russo, Zheng Wen
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Deep Optimal Stopping Sebastian Becker, Patrick Cheridito, Arnulf Jentzen
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Deep Reinforcement Learning for Swarm Systems Maximilian Hüttenrauch, Adrian Šošić, Gerhard Neumann
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Delay and Cooperation in Nonstochastic Bandits Nicolò Cesa-Bianchi, Claudio Gentile, Yishay Mansour
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Dependent Relevance Determination for Smooth and Structured Sparse Regression Anqi Wu, Oluwasanmi Koyejo, Jonathan Pillow
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Determinantal Point Processes for Coresets Nicolas Tremblay, Simon Barthelmé, Pierre-Olivier Amblard
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Determining the Number of Latent Factors in Statistical Multi-Relational Learning Chengchun Shi, Wenbin Lu, Rui Song
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Differentiable Game Mechanics Alistair Letcher, David Balduzzi, Sébastien Racanière, James Martens, Jakob Foerster, Karl Tuyls, Thore Graepel
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Differentiable Reservoir Computing Lyudmila Grigoryeva, Juan-Pablo Ortega
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Distributed Inference for Linear Support Vector Machine Xiaozhou Wang, Zhuoyi Yang, Xi Chen, Weidong Liu
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DPPy: DPP Sampling with Python Guillaume Gautier, Guillermo Polito, Rémi Bardenet, Michal Valko
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DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization Lin Xiao, Adams Wei Yu, Qihang Lin, Weizhu Chen
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Dynamic Pricing in High-Dimensions Adel Javanmard, Hamid Nazerzadeh
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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
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Embarrassingly Parallel Inference for Gaussian Processes Michael Minyi Zhang, Sinead A. Williamson
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Exact Clustering of Weighted Graphs via Semidefinite Programming Aleksis Pirinen, Brendan Ames
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Fairness Constraints: A Flexible Approach for Fair Classification Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi
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Fast Automatic Smoothing for Generalized Additive Models Yousra El-Bachir, Anthony C. Davison
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Forward-Backward Selection with Early Dropping Giorgos Borboudakis, Ioannis Tsamardinos
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Gaussian Processes with Linear Operator Inequality Constraints Christian Agrell
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Generalized Maximum Entropy Estimation Tobias Sutter, David Sutter, Peyman Mohajerin Esfahani, John Lygeros
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Generalized Score Matching for Non-Negative Data Shiqing Yu, Mathias Drton, Ali Shojaie
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Generic Inference in Latent Gaussian Process Models Edwin V. Bonilla, Karl Krauth, Amir Dezfouli
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Graph Reduction with Spectral and Cut Guarantees Andreas Loukas
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Graphical Lasso and Thresholding: Equivalence and Closed-Form Solutions Salar Fattahi, Somayeh Sojoudi
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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
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Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations Alberto Bietti, Julien Mairal
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Hamiltonian Monte Carlo with Energy Conserving Subsampling Khue-Dung Dang, Matias Quiroz, Robert Kohn, Minh-Ngoc Tran, Mattias Villani
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High-Dimensional Poisson Structural Equation Model Learning via $\ell_1$-Regularized Regression Gunwoong Park, Sion Park
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High-Dimensional Varying Index Coefficient Models via Stein's Identity Sen Na, Zhuoran Yang, Zhaoran Wang, Mladen Kolar
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Iterated Learning in Dynamic Social Networks Bernard Chazelle, Chu Wang
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Ivanov-Regularised Least-Squares Estimators over Large RKHSs and Their Interpolation Spaces Stephen Page, Steffen Grünewälder
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Joint PLDA for Simultaneous Modeling of Two Factors Luciana Ferrer, Mitchell McLaren
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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
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Kernels for Sequentially Ordered Data Franz J. Kiraly, Harald Oberhauser
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Layer-Wise Learning Strategy for Nonparametric Tensor Product Smoothing Spline Regression and Graphical Models Kean Ming Tan, Junwei Lu, Tong Zhang, Han Liu
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Lazifying Conditional Gradient Algorithms Gábor Braun, Sebastian Pokutta, Daniel Zink
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Learnability of Solutions to Conjunctive Queries Hubie Chen, Matthew Valeriote
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Learning Attribute Patterns in High-Dimensional Structured Latent Attribute Models Yuqi Gu, Gongjun Xu
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Learning by Unsupervised Nonlinear Diffusion Mauro Maggioni, James M. Murphy
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Learning Optimized Risk Scores Berk Ustun, Cynthia Rudin
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Learning Overcomplete, Low Coherence Dictionaries with Linear Inference Jesse A. Livezey, Alejandro F. Bujan, Friedrich T. Sommer
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Learning Representations of Persistence Barcodes Christoph D. Hofer, Roland Kwitt, Marc Niethammer
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Learning to Match via Inverse Optimal Transport Ruilin Li, Xiaojing Ye, Haomin Zhou, Hongyuan Zha
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Learning Unfaithful $k$-Separable Gaussian Graphical Models De Wen Soh, Sekhar Tatikonda
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Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis Nicolás García Trillos, Daniel Sanz-Alonso, Ruiyi Yang
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Log-Concave Sampling: Metropolis-Hastings Algorithms Are Fast Raaz Dwivedi, Yuansi Chen, Martin J. Wainwright, Bin Yu
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Logical Explanations for Deep Relational Machines Using Relevance Information Ashwin Srinivasan, Lovekesh Vig, Michael Bain
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Low Permutation-Rank Matrices: Structural Properties and Noisy Completion Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
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Matched Bipartite Block Model with Covariates Zahra S. Razaee, Arash A. Amini, Jingyi Jessica Li
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Maximum Likelihood for Gaussian Process Classification and Generalized Linear Mixed Models Under Case-Control Sampling Omer Weissbrod, Shachar Kaufman, David Golan, Saharon Rosset
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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
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Minimal Sample Subspace Learning: Theory and Algorithms Zhenyue Zhang, Yuqing Xia
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Model Selection in Bayesian Neural Networks via Horseshoe Priors Soumya Ghosh, Jiayu Yao, Finale Doshi-Velez
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Model Selection via the VC Dimension Merlin Mpoudeu, Bertrand Clarke
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Model-Free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-Efficient Kernel PCA Ji Chen, Xiaodong Li
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Monotone Learning with Rectified Wire Networks Veit Elser, Dan Schmidt, Jonathan Yedidia
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More Efficient Estimation for Logistic Regression with Optimal Subsamples HaiYing Wang
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Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning Daniel C. Castro, Jeremy Tan, Bernhard Kainz, Ender Konukoglu, Ben Glocker
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Multi-Class Heterogeneous Domain Adaptation Joey Tianyi Zhou, Ivor W. Tsang, Sinno Jialin Pan, Mingkui Tan
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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
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Multiclass Boosting: Margins, Codewords, Losses, and Algorithms Mohammad Saberian, Nuno Vasconcelos
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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
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Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices Zengfeng Huang
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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
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NetSDM: Semantic Data Mining with Network Analysis Jan Kralj, Marko Robnik-Sikonja, Nada Lavrac
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Neural Architecture Search: A Survey Thomas Elsken, Jan Hendrik Metzen, Frank Hutter
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Neural Empirical Bayes Saeed Saremi, Aapo Hyvärinen
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New Convergence Aspects of Stochastic Gradient Algorithms Lam M. Nguyen, Phuong Ha Nguyen, Peter Richtárik, Katya Scheinberg, Martin Takáč, Marten van Dijk
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No-Regret Bayesian Optimization with Unknown Hyperparameters Felix Berkenkamp, Angela P. Schoellig, Andreas Krause
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Non-Convex Matrix Completion and Related Problems via Strong Duality Maria-Florina Balcan, Yingyu Liang, Zhao Song, David P. Woodruff, Hongyang Zhang
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Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression Han Chen, Garvesh Raskutti, Ming Yuan
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Nonparametric Bayesian Aggregation for Massive Data Zuofeng Shang, Botao Hao, Guang Cheng
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Nonparametric Estimation of Probability Density Functions of Random Persistence Diagrams Vasileios Maroulas, Joshua L Mike, Christopher Oballe
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Nonuniformity of P-Values Can Occur Early in Diverging Dimensions Yingying Fan, Emre Demirkaya, Jinchi Lv
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On Asymptotic and Finite-Time Optimality of Bayesian Predictors Daniil Ryabko
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On Consistent Vertex Nomination Schemes Vince Lyzinski, Keith Levin, Carey E. Priebe
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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
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On the Optimality of the Hedge Algorithm in the Stochastic Regime Jaouad Mourtada, Stéphane Gaïffas
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Optimal Convergence Rates for Convex Distributed Optimization in Networks Kevin Scaman, Francis Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié
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Optimal Policies for Observing Time Series and Related Restless Bandit Problems Christopher R. Dance, Tomi Silander
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Optimal Transport: Fast Probabilistic Approximation with Exact Solvers Max Sommerfeld, Jörn Schrieber, Yoav Zemel, Axel Munk
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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
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Parsimonious Online Learning with Kernels via Sparse Projections in Function Space Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro Ribeiro
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Prediction Risk for the Horseshoe Regression Anindya Bhadra, Jyotishka Datta, Yunfan Li, Nicholas G. Polson, Brandon Willard
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Provably Accurate Double-Sparse Coding Thanh V. Nguyen, Raymond K. W. Wong, Chinmay Hegde
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Proximal Distance Algorithms: Theory and Practice Kevin L. Keys, Hua Zhou, Kenneth Lange
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Quantification Under Prior Probability Shift: The Ratio Estimator and Its Extensions Afonso Fernandes Vaz, Rafael Izbicki, Rafael Bassi Stern
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Quantifying Uncertainty in Online Regression Forests Theodore Vasiloudis, Gianmarco De Francisci Morales, Henrik Boström
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Random Feature-Based Online Multi-Kernel Learning in Environments with Unknown Dynamics Yanning Shen, Tianyi Chen, Georgios B. Giannakis
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Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning Can Karakus, Yifan Sun, Suhas Diggavi, Wotao Yin
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Regularization via Mass Transportation Soroosh Shafieezadeh-Abadeh, Daniel Kuhn, Peyman Mohajerin Esfahani
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Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou
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Robust Estimation of Derivatives Using Locally Weighted Least Absolute Deviation Regression WenWu Wang, Ping Yu, Lu Lin, Tiejun Tong
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Robust Frequent Directions with Application in Online Learning Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li, Tong Zhang
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Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf
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Scalable Approximations for Generalized Linear Problems Murat Erdogdu, Mohsen Bayati, Lee H. Dicker
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Scalable Interpretable Multi-Response Regression via SEED Zemin Zheng, M. Taha Bahadori, Yan Liu, Jinchi Lv
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Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds Shusen Wang, Alex Gittens, Michael W. Mahoney
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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
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Semi-Analytic Resampling in Lasso Tomoyuki Obuchi, Yoshiyuki Kabashima
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Shared Subspace Models for Multi-Group Covariance Estimation Alexander M. Franks, Peter Hoff
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Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery Richard Y. Zhang, Somayeh Sojoudi, Javad Lavaei
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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
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Simultaneous Phase Retrieval and Blind Deconvolution via Convex Programming Ali Ahmed, Alireza Aghasi, Paul Hand
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Simultaneous Private Learning of Multiple Concepts Mark Bun, Kobbi Nissim, Uri Stemmer
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Smooth Neighborhood Recommender Systems Ben Dai, Junhui Wang, Xiaotong Shen, Annie Qu
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Solving the OSCAR and SLOPE Models Using a Semismooth Newton-Based Augmented Lagrangian Method Ziyan Luo, Defeng Sun, Kim-Chuan Toh, Naihua Xiu
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Sparse Kernel Regression with Coefficient-Based $\ell_q-$regularization Lei Shi, Xiaolin Huang, Yunlong Feng, Johan A.K. Suykens
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Spectrum Estimation from a Few Entries Ashish Khetan, Sewoong Oh
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Spurious Valleys in One-Hidden-Layer Neural Network Optimization Landscapes Luca Venturi, Afonso S. Bandeira, Joan Bruna
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Stochastic Canonical Correlation Analysis Chao Gao, Dan Garber, Nathan Srebro, Jialei Wang, Weiran Wang
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Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations Qianxiao Li, Cheng Tai, Weinan E
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Stochastic Variance-Reduced Cubic Regularization Methods Dongruo Zhou, Pan Xu, Quanquan Gu
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Streaming Principal Component Analysis from Incomplete Data Armin Eftekhari, Gregory Ongie, Laura Balzano, Michael B. Wakin
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The Common-Directions Method for Regularized Empirical Risk Minimization Po-Wei Wang, Ching-pei Lee, Chih-Jen Lin
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The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks Arjun Sondhi, Ali Shojaie
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The Relationship Between Agnostic Selective Classification, Active Learning and the Disagreement Coefficient Roei Gelbhart, Ran El-Yaniv
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The Sup-Norm Perturbation of HOSVD and Low Rank Tensor Denoising Dong Xia, Fan Zhou
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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
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Tight Lower Bounds on the VC-Dimension of Geometric Set Systems Mónika Csikós, Nabil H. Mustafa, Andrey Kupavskii
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Time-to-Event Prediction with Neural Networks and Cox Regression Håvard Kvamme, Ørnulf Borgan, Ida Scheel
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Train and Test Tightness of LP Relaxations in Structured Prediction Ofer Meshi, Ben London, Adrian Weller, David Sontag
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Transport Analysis of Infinitely Deep Neural Network Sho Sonoda, Noboru Murata
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Tunability: Importance of Hyperparameters of Machine Learning Algorithms Philipp Probst, Anne-Laure Boulesteix, Bernd Bischl
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Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets Jie Wang, Zhanqiu Zhang, Jieping Ye
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Unsupervised Basis Function Adaptation for Reinforcement Learning Edward Barker, Charl Ras
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Unsupervised Evaluation and Weighted Aggregation of Ranked Classification Predictions Mehmet Eren Ahsen, Robert M Vogel, Gustavo A Stolovitzky
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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
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Utilizing Second Order Information in Minibatch Stochastic Variance Reduced Proximal Iterations Jialei Wang, Tong Zhang
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Variance-Based Regularization with Convex Objectives John Duchi, Hongseok Namkoong
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Why Do Deep Convolutional Networks Generalize so Poorly to Small Image Transformations? Aharon Azulay, Yair Weiss
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