Volume 84: International Conference on Artificial Intelligence and Statistics, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands

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Editors: Amos Storkey, Fernando Perez-Cruz

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The Geometry of Random Features

Krzysztof Choromanski, Mark Rowland, Tamas Sarlos, Vikas Sindhwani, Richard Turner, Adrian Weller ; PMLR 84:1-9

Gauged Mini-Bucket Elimination for Approximate Inference

Sungsoo Ahn, Michael Chertkov, Jinwoo Shin, Adrian Weller ; PMLR 84:10-19

A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians

Aleksander Madry, Slobodan Mitrovic, Ludwig Schmidt ; PMLR 84:20-28

An Analysis of Categorical Distributional Reinforcement Learning

Mark Rowland, Marc Bellemare, Will Dabney, Remi Munos, Yee Whye Teh ; PMLR 84:29-37

Combinatorial Preconditioners for Proximal Algorithms on Graphs

Thomas Möllenhoff, Zhenzhang Ye, Tao Wu, Daniel Cremers ; PMLR 84:38-47

Growth-Optimal Portfolio Selection under CVaR Constraints

Guy Uziel, Ran El-Yaniv ; PMLR 84:48-57

Accelerated Stochastic Power Iteration

Peng Xu, Bryan He, Christopher De Sa, Ioannis Mitliagkas, Chris Re ; PMLR 84:58-67

Multi-scale Nystrom Method

Woosang Lim, Rundong Du, Bo Dai, Kyomin Jung, Le Song, Haesun Park ; PMLR 84:68-76

Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach

Satoshi Hara, Kohei Hayashi ; PMLR 84:77-85

Mixed Membership Word Embeddings for Computational Social Science

James Foulds ; PMLR 84:86-95

Fast Threshold Tests for Detecting Discrimination

Emma Pierson, Sam Corbett-Davies, Sharad Goel ; PMLR 84:96-105

Iterative Supervised Principal Components

Juho Piironen, Aki Vehtari ; PMLR 84:106-114

Iterative Spectral Method for Alternative Clustering

Chieh Wu, Stratis Ioannidis, Mario Sznaier, Xiangyu Li, David Kaeli, Jennifer Dy ; PMLR 84:115-123

Can clustering scale sublinearly with its clusters? A variational EM acceleration of GMMs and k-means

Dennis Forster, Jörg Lücke ; PMLR 84:124-132

Parallelised Bayesian Optimisation via Thompson Sampling

Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos ; PMLR 84:133-142

On the challenges of learning with inference networks on sparse, high-dimensional data

Rahul Krishnan, Dawen Liang, Matthew Hoffman ; PMLR 84:143-151

Post Selection Inference with Kernels

Makoto Yamada, Yuta Umezu, Kenji Fukumizu, Ichiro Takeuchi ; PMLR 84:152-160

On how complexity affects the stability of a predictor

Joel Ratsaby ; PMLR 84:161-167

On Truly Block Eigensolvers via Riemannian Optimization

Zhiqiang Xu, Xin Gao ; PMLR 84:168-177

Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond

Heng Guo, Kaan Kara, Ce Zhang ; PMLR 84:178-187

IHT dies hard: Provable accelerated Iterative Hard Thresholding

Rajiv Khanna, Anastasios Kyrillidis ; PMLR 84:188-198

Finding Global Optima in Nonconvex Stochastic Semidefinite Optimization with Variance Reduction

Jinshan Zeng, Ke Ma, Yuan Yao ; PMLR 84:199-207

Outlier Detection and Robust Estimation in Nonparametric Regression

Dehan Kong, Howard Bondell, Weining Shen ; PMLR 84:208-216

Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis

Luca Ambrogioni, Eric Maris ; PMLR 84:217-225

AdaGeo: Adaptive Geometric Learning for Optimization and Sampling

Gabriele Abbati, Alessandra Tosi, Michael Osborne, Seth Flaxman ; PMLR 84:226-234

Online Learning with Non-Convex Losses and Non-Stationary Regret

Xiand Gao, Xiaobo Li, Shuzhong Zhang ; PMLR 84:235-243

Learning Determinantal Point Processes in Sublinear Time

Christophe Dupuy, Francis Bach ; PMLR 84:244-257

Nonlinear Structured Signal Estimation in High Dimensions via Iterative Hard Thresholding

Kaiqing Zhang, Zhuoran Yang, Zhaoran Wang ; PMLR 84:258-268

Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis

Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra ; PMLR 84:269-278

Online Boosting Algorithms for Multi-label Ranking

Young Hun Jung, Ambuj Tewari ; PMLR 84:279-287

Zeroth-Order Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications

Sijia Liu, Jie Chen, Pin-Yu Chen, Alfred Hero ; PMLR 84:288-297

High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups

Paul Rolland, Jonathan Scarlett, Ilija Bogunovic, Volkan Cevher ; PMLR 84:298-307

Robust Active Label Correction

Jan Kremer, Fei Sha, Christian Igel ; PMLR 84:308-316

Factorial HMMs with Collapsed Gibbs Sampling for Optimizing Long-term HIV Therapy

Amit Gruber, Chen Yanover, Tal El-Hay, Anders Sönnerborg, Vanni Borghi, Francesca Incardona, Yaara Goldschmidt ; PMLR 84:317-326

Optimal Submodular Extensions for Marginal Estimation

Pankaj Pansari, Chris Russell, M Pawan Kumar ; PMLR 84:327-335

Semi-Supervised Learning with Competitive Infection Models

Nir Rosenfeld, Amir Globerson ; PMLR 84:336-346

Discriminative Learning of Prediction Intervals

Nir Rosenfeld, Yishay Mansour, Elad Yom-Tov ; PMLR 84:347-355

Topic Compositional Neural Language Model

Wenlin Wang, Zhe Gan, Wenqi Wang, Dinghan Shen, Jiaji Huang, Wei Ping, Sanjeev Satheesh, Lawrence Carin ; PMLR 84:356-365

Learning Priors for Invariance

Eric Nalisnick, Padhraic Smyth ; PMLR 84:366-375

Optimal Cooperative Inference

Scott Cheng-Hsin Yang, Yue Yu, arash Givchi, Pei Wang, Wai Keen Vong, Patrick Shafto ; PMLR 84:376-385

Stochastic Multi-armed Bandits in Constant Space

David Liau, Zhao Song, Eric Price, Ger Yang ; PMLR 84:386-394

Matrix completability analysis via graph k-connectivity

Dehua Cheng, Natali Ruchansky, Yan Liu ; PMLR 84:395-403

FLAG n’ FLARE: Fast Linearly-Coupled Adaptive Gradient Methods

Xiang Cheng, Fred Roosta, Stefan Palombo, Peter Bartlett, Michael Mahoney ; PMLR 84:404-414

Multi-view Metric Learning in Vector-valued Kernel Spaces

Riikka Huusari, Hachem Kadri, Cécile Capponi ; PMLR 84:415-424

Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data

William Herlands, Edward McFowland, Andrew Wilson, Daniel Neill ; PMLR 84:425-434

Dropout as a Low-Rank Regularizer for Matrix Factorization

Jacopo Cavazza, Pietro Morerio, Benjamin Haeffele, Connor Lane, Vittorio Murino, Rene Vidal ; PMLR 84:435-444

A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer

Tianbao Yang, Zhe Li, Lijun Zhang ; PMLR 84:445-453

Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables

Masaaki Takada, Taiji Suzuki, Hironori Fujisawa ; PMLR 84:454-463

Boosting Variational Inference: an Optimization Perspective

Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Ratsch ; PMLR 84:464-472

Personalized and Private Peer-to-Peer Machine Learning

Aurélien Bellet, Rachid Guerraoui, Mahsa Taziki, Marc Tommasi ; PMLR 84:473-481

Tensor Regression Meets Gaussian Processes

Rose Yu, Guangyu Li, Yan Liu ; PMLR 84:482-490

A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization

Emanuel Laude, Tao Wu, Daniel Cremers ; PMLR 84:491-499

Medoids in Almost-Linear Time via Multi-Armed Bandits

Vivek Bagaria, Govinda Kamath, Vasilis Ntranos, Martin Zhang, David Tse ; PMLR 84:500-509

Regional Multi-Armed Bandits

Zhiyang Wang, Ruida Zhou, Cong Shen ; PMLR 84:510-518

Nearly second-order optimality of online joint detection and estimation via one-sample update schemes

Yang Cao, Liyan Xie, Yao Xie, Huan Xu ; PMLR 84:519-528

Sum-Product-Quotient Networks

Or Sharir, Amnon Shashua ; PMLR 84:529-537

Exploiting Strategy-Space Diversity for Batch Bayesian Optimization

Sunil Gupta, Alistair Shilton, Santu Rana, Svetha Venkatesh ; PMLR 84:538-547

Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing Methods

Stephan Clémençon, François Portier ; PMLR 84:548-556

Group invariance principles for causal generative models

Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing ; PMLR 84:557-565

A Provable Algorithm for Learning Interpretable Scoring Systems

Nataliya Sokolovska, Yann Chevaleyre, Jean-Daniel Zucker ; PMLR 84:566-574

Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes

Hyunjik Kim, Yee Whye Teh ; PMLR 84:575-584

Efficient Bandit Combinatorial Optimization Algorithm with Zero-suppressed Binary Decision Diagrams

Shinsaku Sakaue, Masakazu Ishihata, Shin-ichi Minato ; PMLR 84:585-594

Transfer Learning on fMRI Datasets

Hejia Zhang, Po-Hsuan Chen, Peter Ramadge ; PMLR 84:595-603

An Optimization Approach to Learning Falling Rule Lists

Chaofan Chen, Cynthia Rudin ; PMLR 84:604-612

Catalyst for Gradient-based Nonconvex Optimization

Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy, Julien Mairal, Zaid Harchaoui ; PMLR 84:613-622

Benefits from Superposed Hawkes Processes

Hongteng Xu, Dixin Luo, Xu Chen, Lawrence Carin ; PMLR 84:623-631

Nonparametric Preference Completion

Julian Katz-Samuels, Clayton Scott ; PMLR 84:632-641

Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training

Mathieu Sinn, Ambrish Rawat ; PMLR 84:642-651

Efficient and principled score estimation with Nyström kernel exponential families

Dougal Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton ; PMLR 84:652-660

Symmetric Variational Autoencoder and Connections to Adversarial Learning

Liqun Chen, Shuyang Dai, Yunchen Pu, Erjin Zhou, Chunyuan Li, Qinliang Su, Changyou Chen, Lawrence Carin ; PMLR 84:661-669

Few-shot Generative Modelling with Generative Matching Networks

Sergey Bartunov, Dmitry Vetrov ; PMLR 84:670-678

Nonlinear Weighted Finite Automata

Tianyu Li, Guillaume Rabusseau, Doina Precup ; PMLR 84:679-688

Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models

Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman ; PMLR 84:689-697

Variational inference for the multi-armed contextual bandit

Iñigo Urteaga, Chris Wiggins ; PMLR 84:698-706

Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods

Robert Gower, Nicolas Le Roux, Francis Bach ; PMLR 84:707-715

Subsampling for Ridge Regression via Regularized Volume Sampling

Michal Derezinski, Manfred Warmuth ; PMLR 84:716-725

Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition

Pavel Izmailov, Alexander Novikov, Dmitry Kropotov ; PMLR 84:726-735

Batch-Expansion Training: An Efficient Optimization Framework

Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer ; PMLR 84:736-744

Batched Large-scale Bayesian Optimization in High-dimensional Spaces

Zi Wang, Clement Gehring, Pushmeet Kohli, Stefanie Jegelka ; PMLR 84:745-754

Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series

Feras Saad, Vikash Mansinghka ; PMLR 84:755-764

Stochastic Three-Composite Convex Minimization with a Linear Operator

Renbo Zhao, Volkan Cevher ; PMLR 84:765-774

Direct Learning to Rank And Rerank

Cynthia Rudin, Yining Wang ; PMLR 84:775-783

One-shot Coresets: The Case of k-Clustering

Olivier Bachem, Mario Lucic, Silvio Lattanzi ; PMLR 84:784-792

Random Warping Series: A Random Features Method for Time-Series Embedding

Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, Qi Lei, Michael Witbrock ; PMLR 84:793-802

Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD

Sanghamitra Dutta, Gauri Joshi, Soumyadip Ghosh, Parijat Dube, Priya Nagpurkar ; PMLR 84:803-812

Variational Inference based on Robust Divergences

Futoshi Futami, Issei Sato, Masashi Sugiyama ; PMLR 84:813-822

Variational Rejection Sampling

Aditya Grover, Ramki Gummadi, Miguel Lazaro-Gredilla, Dale Schuurmans, Stefano Ermon ; PMLR 84:823-832

Best arm identification in multi-armed bandits with delayed feedback

Aditya Grover, Todor Markov, Peter Attia, Norman Jin, Nicolas Perkins, Bryan Cheong, Michael Chen, Zi Yang, Stephen Harris, William Chueh, Stefano Ermon ; PMLR 84:833-842

A fully adaptive algorithm for pure exploration in linear bandits

Liyuan Xu, Junya Honda, Masashi Sugiyama ; PMLR 84:843-851

Contextual Bandits with Stochastic Experts

Rajat Sen, Karthikeyan Shanmugam, Sanjay Shakkottai ; PMLR 84:852-861

Human Interaction with Recommendation Systems

Sven Schmit, Carlos Riquelme ; PMLR 84:862-870

Community Detection in Hypergraphs: Optimal Statistical Limit and Efficient Algorithms

I Chien, Chung-Yi Lin, I-Hsiang Wang ; PMLR 84:871-879

Smooth and Sparse Optimal Transport

Mathieu Blondel, Vivien Seguy, Antoine Rolet ; PMLR 84:880-889

Robust Maximization of Non-Submodular Objectives

Ilija Bogunovic, Junyao Zhao, Volkan Cevher ; PMLR 84:890-899

Cause-Effect Inference by Comparing Regression Errors

Patrick Bloebaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schoelkopf ; PMLR 84:900-909

Tree-based Bayesian Mixture Model for Competing Risks

Alexis Bellot, Mihaela Schaar ; PMLR 84:910-918

Actor-Critic Fictitious Play in Simultaneous Move Multistage Games

Julien Perolat, Bilal Piot, Olivier Pietquin ; PMLR 84:919-928

Random Subspace with Trees for Feature Selection Under Memory Constraints

Antonio Sutera, Célia Châtel, Gilles Louppe, Louis Wehenkel, Pierre Geurts ; PMLR 84:929-937

Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information

Jakob Runge ; PMLR 84:938-947

Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures

Tomi Silander, Janne Leppä-aho, Elias Jääsaari, Teemu Roos ; PMLR 84:948-957

Convex Optimization over Intersection of Simple Sets: improved Convergence Rate Guarantees via an Exact Penalty Approach

Achintya Kundu, Francis Bach, Chiranjib Bhattacharya ; PMLR 84:958-967

Variational Sequential Monte Carlo

Christian Naesseth, Scott Linderman, Rajesh Ranganath, David Blei ; PMLR 84:968-977

Statistically Efficient Estimation for Non-Smooth Probability Densities

Masaaki Imaizumi, Takanori Maehara, Yuichi Yoshida ; PMLR 84:978-987

SDCA-Powered Inexact Dual Augmented Lagrangian Method for Fast CRF Learning

Xu Hu, Guillaume Obozinski ; PMLR 84:988-997

Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression

Mathurin Massias, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon ; PMLR 84:998-1007

Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models

Atsushi Nitanda, Taiji Suzuki ; PMLR 84:1008-1016

Statistical Sparse Online Regression: A Diffusion Approximation Perspective

Jianqing Fan, Wenyan Gong, Chris Junchi Li, Qiang Sun ; PMLR 84:1017-1026

Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization

Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin Kai Wing Ng, Yuichi Yoshida ; PMLR 84:1027-1036

Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs

Lawrence Murray, Daniel Lundén, Jan Kudlicka, David Broman, Thomas Schön ; PMLR 84:1037-1046

Learning to Round for Discrete Labeling Problems

Pritish Mohapatra, Jawahar C.V., M Pawan Kumar ; PMLR 84:1047-1056

Approximate Ranking from Pairwise Comparisons

Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin Wainwright ; PMLR 84:1057-1066

Semi-Supervised Prediction-Constrained Topic Models

Michael Hughes, Gabriel Hope, Leah Weiner, Thomas McCoy, Roy Perlis, Erik Sudderth, Finale Doshi-Velez ; PMLR 84:1067-1076

A Stochastic Differential Equation Framework for Guiding Online User Activities in Closed Loop

Yichen Wang, Evangelos Theodorou, Apurv Verma, Le Song ; PMLR 84:1077-1086

Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms

Pan Xu, Tianhao Wang, Quanquan Gu ; PMLR 84:1087-1096

A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery

Xiao Zhang, Lingxiao Wang, Quanquan Gu ; PMLR 84:1097-1107

Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling

Hongyi Ding, Mohammad Khan, Issei Sato, Masashi Sugiyama ; PMLR 84:1108-1116

Factor Analysis on a Graph

Masayuki Karasuyama, Hiroshi Mamitsuka ; PMLR 84:1117-1126

Crowdclustering with Partition Labels

Junxiang Chen, Yale Chang, Peter Castaldi, Michael Cho, Brian Hobbs, Jennifer Dy ; PMLR 84:1127-1136

Learning Structural Weight Uncertainty for Sequential Decision-Making

Ruiyi Zhang, Chunyuan Li, Changyou Chen, Lawrence Carin ; PMLR 84:1137-1146

Towards Memory-Friendly Deterministic Incremental Gradient Method

Jiahao Xie, Hui Qian, Zebang Shen, Chao Zhang ; PMLR 84:1147-1156

Optimality of Approximate Inference Algorithms on Stable Instances

Hunter Lang, David Sontag, Aravindan Vijayaraghavan ; PMLR 84:1157-1166

Bayesian Approaches to Distribution Regression

Ho Chung Leon Law, Dougal Sutherland, Dino Sejdinovic, Seth Flaxman ; PMLR 84:1167-1176

Submodularity on Hypergraphs: From Sets to Sequences

Marko Mitrovic, Moran Feldman, Andreas Krause, Amin Karbasi ; PMLR 84:1177-1184

Provable Estimation of the Number of Blocks in Block Models

Bowei Yan, Purnamrita Sarkar, Xiuyuan Cheng ; PMLR 84:1185-1194

Differentially Private Regression with Gaussian Processes

Michael Smith, Mauricio Álvarez, Max Zwiessele, Neil D. Lawrence ; PMLR 84:1195-1203

Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems

Sai Praneeth Reddy Karimireddy, Sebastian Stich, Martin Jaggi ; PMLR 84:1204-1213

VAE with a VampPrior

Jakub Tomczak, Max Welling ; PMLR 84:1214-1223

Structured Factored Inference for Probabilistic Programming

Avi Pfeffer, Brian Ruttenberg, William Kretschmer, Alison OConnor ; PMLR 84:1224-1232

A Generic Approach for Escaping Saddle points

Sashank Reddi, Manzil Zaheer, Suvrit Sra, Barnabas Poczos, Francis Bach, Ruslan Salakhutdinov, Alex Smola ; PMLR 84:1233-1242

Policy Evaluation and Optimization with Continuous Treatments

Nathan Kallus, Angela Zhou ; PMLR 84:1243-1251

Multiphase MCMC Sampling for Parameter Inference in Nonlinear Ordinary Differential Equations

Alan Lazarus, Dirk Husmeier, Theodore Papamarkou ; PMLR 84:1252-1260

Why Adaptively Collected Data Have Negative Bias and How to Correct for It

Xinkun Nie, Xiaoying Tian, Jonathan Taylor, James Zou ; PMLR 84:1261-1269

Sparse Linear Isotonic Models

Sheng Chen, Arindam Banerjee ; PMLR 84:1270-1279

Robustness of classifiers to uniform $\ell_p$ and Gaussian noise

Jean-Yves Franceschi, Alhussein Fawzi, Omar Fawzi ; PMLR 84:1280-1288

Nested CRP with Hawkes-Gaussian Processes

Xi Tan, Vinayak Rao, Jennifer Neville ; PMLR 84:1289-1298

Sketching for Kronecker Product Regression and P-splines

Huaian Diao, Zhao Song, Wen Sun, David Woodruff ; PMLR 84:1299-1308

Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models

Ardavan Saeedi, Matthew Hoffman, Stephen DiVerdi, Asma Ghandeharioun, Matthew Johnson, Ryan Adams ; PMLR 84:1309-1317

Cheap Checking for Cloud Computing: Statistical Analysis via Annotated Data Streams

Chris Hickey, Graham Cormode ; PMLR 84:1318-1326

Minimax Reconstruction Risk of Convolutional Sparse Dictionary Learning

Shashank Singh, Barnabas Poczos, Jian Ma ; PMLR 84:1327-1336

Kernel Conditional Exponential Family

Michael Arbel, Arthur Gretton ; PMLR 84:1337-1346

Linear Stochastic Approximation: How Far Does Constant Step-Size and Iterate Averaging Go?

Chandrashekar Lakshminarayanan, Csaba Szepesvari ; PMLR 84:1347-1355

Stochastic Zeroth-order Optimization in High Dimensions

Yining Wang, Simon Du, Sivaraman Balakrishnan, Aarti Singh ; PMLR 84:1356-1365

Teacher Improves Learning by Selecting a Training Subset

Yuzhe Ma, Robert Nowak, Philippe Rigollet, Xuezhou Zhang, Xiaojin Zhu ; PMLR 84:1366-1375

Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation

Penporn Koanantakool, Alnur Ali, Ariful Azad, Aydin Buluc, Dmitriy Morozov, Leonid Oliker, Katherine Yelick, Sang-Yun Oh ; PMLR 84:1376-1386

Robust Vertex Enumeration for Convex Hulls in High Dimensions

Pranjal Awasthi, Bahman Kalantari, Yikai Zhang ; PMLR 84:1387-1396

Fast generalization error bound of deep learning from a kernel perspective

Taiji Suzuki ; PMLR 84:1397-1406

Product Kernel Interpolation for Scalable Gaussian Processes

Jacob Gardner, Geoff Pleiss, Ruihan Wu, Kilian Weinberger, Andrew Wilson ; PMLR 84:1407-1416

Towards Provable Learning of Polynomial Neural Networks Using Low-Rank Matrix Estimation

Mohammadreza Soltani, Chinmay Hegde ; PMLR 84:1417-1426

Scalable Generalized Dynamic Topic Models

Patrick Jähnichen, Florian Wenzel, Marius Kloft, Stephan Mandt ; PMLR 84:1427-1435

Bayesian Structure Learning for Dynamic Brain Connectivity

Michael Andersen, Ole Winther, Lars Kai Hansen, Russell Poldrack, Oluwasanmi Koyejo ; PMLR 84:1436-1446

Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method

Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro ; PMLR 84:1447-1455

Frank-Wolfe Splitting via Augmented Lagrangian Method

Gauthier Gidel, Fabian Pedregosa, Simon Lacoste-Julien ; PMLR 84:1456-1465

Learning linear structural equation models in polynomial time and sample complexity

Asish Ghoshal, Jean Honorio ; PMLR 84:1466-1475

Convergence diagnostics for stochastic gradient descent with constant learning rate

Jerry Chee, Panos Toulis ; PMLR 84:1476-1485

Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity

Asish Ghoshal, Jean Honorio ; PMLR 84:1486-1494

Nonparametric Sharpe Ratio Function Estimation in Heteroscedastic Regression Models via Convex Optimization

Seung-Jean Kim, Johan Lim, Joong-Ho Won ; PMLR 84:1495-1504

Stochastic algorithms for entropy-regularized optimal transport problems

Brahim Khalil Abid, Robert Gower ; PMLR 84:1505-1512

Plug-in Estimators for Conditional Expectations and Probabilities

Steffen Grunewalder ; PMLR 84:1513-1521

Factorized Recurrent Neural Architectures for Longer Range Dependence

Francois Belletti, Alex Beutel, Sagar Jain, Ed Chi ; PMLR 84:1522-1530

On the Statistical Efficiency of Compositional Nonparametric Prediction

Yixi Xu, Jean Honorio, Xiao Wang ; PMLR 84:1531-1539

Metrics for Deep Generative Models

Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, Patrick Smagt ; PMLR 84:1540-1550

Combinatorial Penalties: Which structures are preserved by convex relaxations?

Marwa El Halabi, Francis Bach, Volkan Cevher ; PMLR 84:1551-1560

Generalized Binary Search For Split-Neighborly Problems

Stephen Mussmann, Percy Liang ; PMLR 84:1561-1569

Intersection-Validation: A Method for Evaluating Structure Learning without Ground Truth

Jussi Viinikka, Ralf Eggeling, Mikko Koivisto ; PMLR 84:1570-1578

On Statistical Optimality of Variational Bayes

Debdeep Pati, Anirban Bhattacharya, Yun Yang ; PMLR 84:1579-1588

Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems

Jason Ge, Zhaoran Wang, Mengdi Wang, Han Liu ; PMLR 84:1589-1598

Online Regression with Partial Information: Generalization and Linear Projection

Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken-Ichi Kawarabayashi ; PMLR 84:1599-1607

Learning Generative Models with Sinkhorn Divergences

Aude Genevay, Gabriel Peyre, Marco Cuturi ; PMLR 84:1608-1617

Reparameterizing the Birkhoff Polytope for Variational Permutation Inference

Scott Linderman, Gonzalo Mena, Hal Cooper, Liam Paninski, John Cunningham ; PMLR 84:1618-1627

Achieving the time of 1-NN, but the accuracy of k-NN

Lirong Xue, Samory Kpotufe ; PMLR 84:1628-1636

Efficient Weight Learning in High-Dimensional Untied MLNs

Khan Mohammad Al Farabi, Somdeb Sarkhel, Deepak Venugopal ; PMLR 84:1637-1645

Learning with Complex Loss Functions and Constraints

Harikrishna Narasimhan ; PMLR 84:1646-1654

Solving lp-norm regularization with tensor kernels

Saverio Salzo, Lorenzo Rosasco, Johan Suykens ; PMLR 84:1655-1663

Weighted Tensor Decomposition for Learning Latent Variables with Partial Data

Omer Gottesman, Weiwei Pan, Finale Doshi-Velez ; PMLR 84:1664-1672

Multi-objective Contextual Bandit Problem with Similarity Information

Eralp Turgay, Doruk Oner, Cem Tekin ; PMLR 84:1673-1681

Turing: A Language for Flexible Probabilistic Inference

Hong Ge, Kai Xu, Zoubin Ghahramani ; PMLR 84:1682-1690

Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure

Beilun Wang, arshdeep Sekhon, Yanjun Qi ; PMLR 84:1691-1700

Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control

Sanket Kamthe, Marc Deisenroth ; PMLR 84:1701-1710

Approximate Bayesian Computation with Kullback-Leibler Divergence as Data Discrepancy

Bai Jiang ; PMLR 84:1711-1721

Practical Bayesian optimization in the presence of outliers

Ruben Martinez-Cantin, Kevin Tee, Michael McCourt ; PMLR 84:1722-1731

Competing with Automata-based Expert Sequences

Mehryar Mohri, Scott Yang ; PMLR 84:1732-1740

Reducing Crowdsourcing to Graphon Estimation, Statistically

Devavrat Shah, Christina Lee ; PMLR 84:1741-1750

Robust Locally-Linear Controllable Embedding

Ershad Banijamali, Rui Shu, mohammad Ghavamzadeh, Hung Bui, Ali Ghodsi ; PMLR 84:1751-1759

Combinatorial Semi-Bandits with Knapsacks

Karthik Abinav Sankararaman, Aleksandrs Slivkins ; PMLR 84:1760-1770

Structured Optimal Transport

David Alvarez-Melis, Tommi Jaakkola, Stefanie Jegelka ; PMLR 84:1771-1780

Graphical Models for Non-Negative Data Using Generalized Score Matching

Shiqing Yu, Mathias Drton, Ali Shojaie ; PMLR 84:1781-1790

Asynchronous Doubly Stochastic Group Regularized Learning

Bin Gu, Zhouyuan Huo, Heng Huang ; PMLR 84:1791-1800

Convergence of Value Aggregation for Imitation Learning

Ching-An Cheng, Byron Boots ; PMLR 84:1801-1809

Inference in Sparse Graphs with Pairwise Measurements and Side Information

Dylan Foster, Karthik Sridharan, Daniel Reichman ; PMLR 84:1810-1818

Parallel and Distributed MCMC via Shepherding Distributions

Arkabandhu Chowdhury, Christopher Jermaine ; PMLR 84:1819-1827

The Power Mean Laplacian for Multilayer Graph Clustering

Pedro Mercado, Antoine Gautier, Francesco Tudisco, Matthias Hein ; PMLR 84:1828-1838

Adaptive Sampling for Coarse Ranking

Sumeet Katariya, Lalit Jain, Nandana Sengupta, James Evans, Robert Nowak ; PMLR 84:1839-1848

Comparison Based Learning from Weak Oracles

Ehsan Kazemi, Lin Chen, Sanjoy Dasgupta, Amin Karbasi ; PMLR 84:1849-1858

The Binary Space Partitioning-Tree Process

Xuhui Fan, Bin Li, Scott Sisson ; PMLR 84:1859-1867

On denoising modulo 1 samples of a function

Mihai Cucuringu, Hemant Tyagi ; PMLR 84:1868-1876

Scalable Hash-Based Estimation of Divergence Measures

Morteza Noshad, Alfred Hero ; PMLR 84:1877-1885

Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap

Aryan Mokhtari, Hamed Hassani, Amin Karbasi ; PMLR 84:1886-1895

Online Continuous Submodular Maximization

Lin Chen, Hamed Hassani, Amin Karbasi ; PMLR 84:1896-1905

Efficient Bayesian Methods for Counting Processes in Partially Observable Environments

Ferdian Jovan, Jeremy Wyatt, Nick Hawes ; PMLR 84:1906-1913

Matrix-normal models for fMRI analysis

Michael Shvartsman, Narayanan Sundaram, Mikio Aoi, Adam Charles, Theodore Willke, Jonathan Cohen ; PMLR 84:1914-1923

The emergence of spectral universality in deep networks

Jeffrey Pennington, Samuel Schoenholz, Surya Ganguli ; PMLR 84:1924-1932

Spectral Algorithms for Computing Fair Support Vector Machines

Mahbod Olfat, Anil Aswani ; PMLR 84:1933-1942

Bayesian Multi-label Learning with Sparse Features and Labels, and Label Co-occurrences

He Zhao, Piyush Rai, Lan Du, Wray Buntine ; PMLR 84:1943-1951

Nonparametric Bayesian sparse graph linear dynamical systems

Rahi Kalantari, Joydeep Ghosh, Mingyuan Zhou ; PMLR 84:1952-1960

Proximity Variational Inference

Jaan Altosaar, Rajesh Ranganath, David Blei ; PMLR 84:1961-1969

Near-Optimal Machine Teaching via Explanatory Teaching Sets

Yuxin Chen, Oisin Mac Aodha, Shihan Su, Pietro Perona, Yisong Yue ; PMLR 84:1970-1978

Learning Hidden Quantum Markov Models

Siddarth Srinivasan, Geoff Gordon, Byron Boots ; PMLR 84:1979-1987

Labeled Graph Clustering via Projected Gradient Descent

Shiau Hong Lim, Gregory Calvez ; PMLR 84:1988-1997

Gradient Diversity: a Key Ingredient for Scalable Distributed Learning

Dong Yin, Ashwin Pananjady, Max Lam, Dimitris Papailiopoulos, Kannan Ramchandran, Peter Bartlett ; PMLR 84:1998-2007

HONES: A Fast and Tuning-free Homotopy Method For Online Newton Step

Yuting Ye, Lihua Lei, Cheng Ju ; PMLR 84:2008-2017

Probability–Revealing Samples

Krzysztof Onak, Xiaorui Sun ; PMLR 84:2018-2026

Derivative Free Optimization Via Repeated Classification

Tatsunori Hashimoto, Steve Yadlowsky, John Duchi ; PMLR 84:2027-2036

Online Ensemble Multi-kernel Learning Adaptive to Non-stationary and Adversarial Environments

Yanning Shen, Tianyi Chen, Georgios Giannakis ; PMLR 84:2037-2046

A Unified Dynamic Approach to Sparse Model Selection

Chendi Huang, Yuan Yao ; PMLR 84:2047-2055

Bootstrapping EM via Power EM and Convergence in the Naive Bayes Model

Costis Daskalakis, Christos Tzamos, Manolis Zampetakis ; PMLR 84:2056-2064

Dimensionality Reduced $\ell^{0}$-Sparse Subspace Clustering

Yingzhen Yang ; PMLR 84:2065-2074

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