[Artificial Intelligence and Statistics Logo] Artificial Intelligence and Statistics 2018


AISTATS 2018 Accepted Papers

The accepted papers can be found in the below table sorted in increasing order of Paper ID.

Paper ID Paper Title Author Names
2 The Geometry of Random Features Krzysztof Choromanski, ; Mark Rowland, University of Cambridge ; Tamas Sarlos, Google Research; Vikas Sindhwani, Google Brain Robotics; Richard Turner, Cambridge; Adrian Weller*, University of Cambridge
4 Gauged Mini-Bucket Elimination for Approximate Inference Sungsoo Ahn, KAIST; Michael Chertkov, Los Alamos National Laborator; Jinwoo Shin, KAIST; Adrian Weller*, University of Cambridge
8 A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians Aleksander Madry, MIT; Slobodan Mitrovic*, EPFL; Ludwig Schmidt, MIT
10 An Analysis of Categorical Distributional Reinforcement Learning Mark Rowland*, University of Cambridge ; Marc Bellemare, Google Brain; Will Dabney, DeepMind; Remi Munos, DeepMind; Yee Whye Teh, Oxford and DeepMind
16 Combinatorial Preconditioners for Proximal Algorithms on Graphs Thomas Möllenhoff*, TU Munich; Zhenzhang Ye, ; Tao Wu, ; Daniel Cremers,
22 Growth-Optimal Portfolio Selection under CVaR Constraints Guy Uziel*, Technion; Ran El-Yaniv, Technion
26 Accelerated Stochastic Power Iteration Christopher De Sa, Cornell University; Bryan He, Stanford University; Ioannis Mitliagkas, Université de Montréal; Chris Re, Stanford University; Peng Xu*, Stanford University
27 Multi-scale Nystrom Method Woosang Lim, Georgia Tech; Rundong Du, Georgia Tech; Bo Dai, Geogia Tech; Kyomin Jung, Seoul National University; Le Song, Georgia Tech; Haesun Park*, Georgia Tech
30 Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach Satoshi Hara*, Osaka University; Kohei Hayashi,
32 Mixed Membership Word Embeddings for Computational Social Science James Foulds*, UMBC
35 Fast Threshold Tests for Detecting Discrimination Emma Pierson*, Stanford University; Sam Corbett-Davies, Stanford University; Sharad Goel,
40 Iterative Supervised Principal Components Juho Piironen*, Aalto University; Aki Vehtari, Aalto
42 Iterative Spectral Method for Alternative Clustering Chieh Wu*, Northeastern University; Stratis Ioannidis, NEU; Mario Sznaier, Northeastern University; Xiangyu Li, Northeastern University; David Kaeli, Northeastern University; Jennifer Dy, North Eastern
45 Can clustering scale sublinearly with its clusters? A variational EM acceleration of GMMs and k-means Dennis Forster*, University of Oldenburg; Jörg Lücke, University of Oldenburg
48 Parallelised Bayesian Optimisation via Thompson Sampling Kirthevasan Kandasamy*, ; Akshay Krishnamurthy, U-Mass Amherst; Jeff Schneider, CMU; Barnabas Poczos, Carnegie Mellon University
49 On the challenges of learning with inference networks on sparse, high-dimensional data Rahul Krishnan*, MIT; Dawen Liang, Netflix; Matthew Hoffman, Google; Matthew Hoffman, Google; Dawen Liang, Netflix
54 Post Selection Inference with Kernels Makoto Yamada*, RIKEN; Yuta Umezu, ; Kenji Fukumizu, ; Ichiro Takeuchi,
55 On how complexity effects the stability of a predictor Joel Ratsaby*, Ariel University
56 On the Truly Block Eigensolvers via First-Order Riemannian Optimization Zhiqiang Xu*, KAUST; Xin Gao,
59 Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond Heng Guo*, University of Edinburgh; Kaan Kara, ETH Zurich; Ce Zhang, ETH Zurich
60 IHT dies hard: Provable accelerated Iterative Hard Thresholding Rajiv Khanna, UT Austin; Anastasios Kyrillidis*, IBM T.J. Watson Research Cente
65 Finding Global Optima in Nonconvex Stochastic Semidefinite Optimization with Variance Reduction Jinshan ZENG*, Hongkong University of Science and Technology; Ke Ma, (IIE, CAS; Yuan Yao, Hongkong University of Science and Techonology
66 Outlier Detection and Robust Estimation in Nonparametric Regression Dehan Kong, Univ. of Toronto; Howard Bondell, North Carolina State University; Weining Shen*, UC Irvine
68 Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis Luca Ambrogioni*, Radboud University; Eric Maris, Radboud University
72 AdaGeo: Adaptive Geometric Learning for Optimization and Sampling Gabriele Abbati*, University of Oxford; Alessandra Tosi, Mind Foundry, Oxford; Seth Flaxman, Imperial College London; Michael Osborne, Oxford
74 Online Learning with Non-Convex Losses and Non-Stationary Regret Xiaobo Li*, University of Minnesota; Xiang Gao, University of Minnesota; Shuzhong Zhang, University of Minnesota
75 Learning Determinantal Point Processes in Sublinear Time Christophe Dupuy*, INRIA; Francis Bach, INRIA - ENS
76 Nonlinear Structured Signal Estimation in High Dimensions via Iterative Hard Thresholding Kaiqing Zhang, University of Illinois at Urba; Zhuoran Yang*, Princeton University
77 Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis Hiroyuki Kasai*, UEC; Hiroyuki Sato, Kyoto University; Bamdev Mishra, Amazon
78 Online Boosting Algorithms for Multi-label Ranking Young Hun Jung*, Universith of Michigan; Ambuj Tewari, Universith of Michigan
80 Zeroth-Order Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications Sijia Liu*, University of Michigan; Jie Chen, ; Pin-Yu Chen, ; Alfred Hero,
86 High-dimensional Bayesian optimization via additive models with overlapping groups Paul Rolland*, EPFL, LIONS; Jonathan Scarlett, ; Ilija Bogunovic, ; Volkan Cevher, EPFL
89 Robust Active Label Correction Jan Kremer, University of Copenhagen; Fei Sha, UCLA; Christian Igel*, University of Copenhagen
90 Factorial HMM with Collapsed Gibbs Sampling for optimizing long-term HIV Therapy Amit Gruber*, IBM Research; Chen Yanover, IBM Research; Tal El-Hay, IBM Research; Yaara Goldschmidt, IBM Research; Anders Sönnerborg, Karolinska Institute, Karolinska University Hospital; Vanni Borghi, Modena University Hospital; Francesca Incardona, EuResist Network GEIE, InformaPro S.r.l.
91 Optimal Submodular Extensions for Marginal Estimation Pankaj Pansari*, University of Oxford; Chris Russell, The Alan Turing Institute; M. Pawan Kumar, University of Oxford
92 Semi-Supervised Learning with Competitive Infection Models Nir Rosenfeld*, Harvard University; Amir Globerson, Tel Aviv University
94 Discriminative Learning of Prediction Intervals Nir Rosenfeld*, Harvard University; Yishay Mansour, Tel Aviv University; Elad Yom Tov, Microsoft Research
95 Topic Compositional Neural Language Model Wenlin Wang*, Duke University; Zhe Gan, Duke University; Wenqi Wang, Purdue University; Dinghan Shen, Duke University; Jiaji Huang, Baidu Silicon Valley Artificial Intelligence Lab; Wei Ping, Baidu Silicon Valley Artificial Intelligence Lab; Sanjeev Satheesh, Baidu Silicon Valley Artificial Intelligence Lab; Lawrence Carin, Duke
97 Learning Priors for Invariance Eric Nalisnick*, UC Irvine; Padhraic Smyth, University of California, Irvine
98 Optimal Cooperative Inference Scott Cheng-Hsin Yang*, Rutgers University--Newark; Yue Yu, Rutgers University--Newark; arash Givchi, Rutgers University--Newark; Pei Wang, Rutgers University--Newark; wai Keen Vong, Rutgers University--Newark; Patrick Shafto, Rutgers University--Newark
102 Stochastic Multi-armed Bandits in Constant Space David Liau, UT-Austin; Zhao Song, UT-Austin; Eric Price, UT-Austin; Ger Yang*, UT-Austin
109 Matrix completability analysis via graph k-connectivity Dehua Cheng*, Univ. of Southern California; Natali Ruchansky, ; Yan Liu, University of Southern California
112 FLAG n’ FLARE: Fast Linearly-Coupled Adaptive Gradient Methods Xiang Cheng, UC Berkeley; Fred Roosta*, University of Queensland; Stefan Palombo, UC Berkeley; Peter Bartlett, UC Berkeley; Michael Mahoney, UC Berkeley
113 Multi-view Metric Learning in Vector-valued Kernel Spaces Riikka Huusari*, Aix-Marseille Université; Hachem Kadri, Aix-Marseille University; Cécile Capponi,
115 Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data William Herlands*, Carnegie Mellon University; Edward McFowland, ; Andrew Wilson, Cornell University; Daniel Neill,
117 Dropout as a Low-Rank Regularizer for Matrix Factorization Jacopo Cavazza*, Istituto Italiano di Tecnologi; Pietro Morerio, Istituto Italiano di Tecnologia; Benjamin Haeffele, Johns Hopkins University; Connor Lane, Johns Hopkins University; Vittorio Murino, Istituto Italiano di Tecnologia; Rene Vidal, Johns Hopkins University
119 A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer Tianbao Yang*, University of Iowa; Zhe Li, ; Lijun Zhang, Nanjing University
120 Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables Masaaki Takada*, The Graduate University for Advanced Studies; Taiji Suzuki, The University of Tokyo; Hironori Fujisawa, The Insitute of Statistical Mathematics
121 Boosting Variational Inference: an Optimization Perspective Francesco Locatello*, ETH Zurich; Rajiv Khanna, UT Austin; Joydeep Ghosh, ; Gunnar Ratsch,
122 Personalized and Private Peer-to-Peer Machine Learning Aurélien Bellet*, INRIA; Rachid Guerraoui, ; mahsa Taziki, ; Marc Tommasi,
125 Tensor Regression Meets Gaussian Processes Rose Yu*, Caltech; Guangyu Li, University of Southern California; Yan Liu, University of Southern California
127 A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization Emanuel Laude*, Technical University of Munich; Tao Wu, ; Daniel Cremers,
133 Medoids in Almost-Linear Time via Multi-Armed Bandits Vivek Bagaria, ; Govinda Kamath, ; Martin Zhang, Stanford University; Vasilis Ntranos, ; David Tse*,
139 Regional Multi-Armed Bandits Zhiyang Wang, USTC; Ruida Zhou, USTC; Cong Shen*, Univ. of Sci. & Tech. China
142 Nearly second-order optimality of online joint detection and estimation via one-sample update schemes Yang Cao*, Georgia Institute of Technolog; Liyan Xie, ; Yao Xie, ; Huan Xu,
151 Sum-Product-Quotient Networks Or Sharir*, Hebrew University of Jerusalem; Amnon Shashua, Hebrew University of Jerusalem
154 Exploiting Strategy-Space Diversity for Batch Bayesian Optimization Sunil Gupta*, Deakin University; Alistair Shilton, Deakin University; Santu Rana, Deakin University; Svetha Venkatesh, Deakin University
158 Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing Methods Stephan Clémençon*, Telecom ParisTech; François Portier, Telecom ParisTech
166 Group invariance principles for causal generative models Michel Besserve*, ; naji Shajarisales, MPI for Intelligent Systems; Bernhard Schoelkopf, MPI for Intelligent Systems; Dominik Janzing, MPI for Intelligent Systems
167 A Provable Algorithm for Learning Interpretable Scoring Systems Nataliya Sokolovska*, University Paris 6; Yann Chevaleyre, University Paris Dauphine; Jean-Daniel Zucker, IRD
172 Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes Hyunjik Kim*, University of Oxford; Yee Whye Teh, Oxford
178 Efficient Bandit Combinatorial Optimization Algorithm with Zero-suppressed Binary Decision Diagrams Shinsaku Sakaue*, NTT; Masakazu Ishihata, Hokkaido University; Shin-ichi Minato,
181 Transfer Learning on fMRI Datasets Hejia Zhang*, Princeton University; Po-Hsuan Chen, Princeton University; Peter Ramadge, Princeton University
183 An Optimization Approach to Learning Falling Rule Lists Chaofan Chen*, Duke University; Cynthia Rudin, Duke
185 Catalyst for Gradient-based Nonconvex Optimization Courtney Paquette*, Ohio State University; Hongzhou Lin, INRIA; Dmitriy Drusvyatskiy, University of Washington; Julien Mairal, Inria; Zaid Harchaoui, University of Washington
188 Benefits from Superposed Hawkes Processes Hongteng Xu*, Duke University; Dixin Luo, ; Xu Chen, Tsinghua University; Lawrence Carin, Duke
192 Nonparametric Preference Completion Julian Katz-Samuels*, University of Michigan; Clayton Scott, University of Michigan
198 Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training Mathieu Sinn*, ; Ambrish Rawat, IBM Research
201 Efficient and principled score estimation with Nyström kernel exponential families Dougal Sutherland*, Gatsby unit, UCL; Heiko Strathmann, ; Michael Arbel, Gatsby unit, UCL; Arthur Gretton, Gatsby unit, UCL
208 Symmetric Variational Autoencoder and Connections to Adversarial Learning Liqun Chen*, Duke University; Shuyang Dai, Duke University; Yunchen Pu, Duke University; Chunyuan Li, Duke University; Qinliang Su, Duke University; Erjin Zhou, Face++; Lawrence Carin, Duke
210 Few-shot Generative Modelling with Generative Matching Networks Sergey Bartunov*, DeepMind; Dmitry Vetrov, Higher School of Economics
211 Nonlinear Weighted Finite Automata Tianyu Li*, McGill University; Guillaume Rabusseau, McGill University; Doina Precup, McGill University
212 Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models Hugh Salimbeni*, Imperial College London; Stefanos Eleftheriadis, Prowler.io; James Hensman, PROWLER.io
216 Variational inference for the multi-armed contextual bandit Iñigo Urteaga*, Columbia University; Chris Wiggins, Columbia University
220 Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods Robert Gower*, Telecom Paristech; Nicolas Le Roux, Google Brain; Francis Bach, Inria / ENS
226 Subsampling for Ridge Regression via Regularized Volume Sampling Michal Derezinski*, UC Santa Cruz; Manfred Warmuth,
228 Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition Pavel Izmailov*, Cornell University; Dmitry Kropotov, MSU; Alexander Novikov, Higher school of economics
229 Batch-Expansion Training: An Efficient Optimization Framework Michal Derezinski*, UC Santa Cruz; Dhruv Mahajan, Facebook Research; S. Sathiya Keerthi, Microsoft Corporation; S. V. N. Vishwanathan, UC Santa Cruz; Markus Weimer, Microsoft Corporation
237 Batched Large-scale Bayesian Optimization in High-dimensional Spaces Zi Wang*, MIT; Clement Gehring, ; Stefanie Jegelka, MIT; Pushmeet Kohli,
244 A Bayesian Nonparametric Method for Clustering Imputation, and Forecasting in Multivariate Time Series FERAS SAAD*, MIT; Vikash Mansinghka, MIT
245 Stochastic Three-Composite Convex Minimization with a Linear Operator Renbo Zhao*, NUS; Volkan Cevher, EPFL
246 Direct Learning to Rank And Rerank Cynthia Rudin*, Duke; Yining Wang, Carnegie Mellon University
247 One-shot Coresets: The Case of k-Clustering Olivier Bachem*, ETH Zurich; Mario Lucic, Google Brain Zurich; Silvio Lattanzi,
249 Random Warping Series: A Random Features Method for Time-Series Embedding Lingfei Wu*, IBM T. J. Watson Research Cent; Ian En-Hsu Yen, CMU; Jinfeng Yi, ; Fangli Xu, College of William and Mary; Qi Lei, University of Texas at Austin; Michael Witbrock, IBM T. J. Watson Research Center
250 Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD Sanghamitra Dutta*, Carnegie Mellon University; Gauri Joshi, Carnegie Mellon University; Soumyadip Ghosh, IBM Research; Parijat Dube, IBM Research; Priya Nagpurkar, IBM Research
251 Variational Inference based on Robust Divergences Futoshi Futami*, The University of Tokyo/RIKEN; Issei Sato, The University of Tokyo / RIKEN; Masashi Sugiyama, RIKEN / The University of Tokyo
255 Resampled Proposal Distributions for Variational Inference and Learning Aditya Grover*, Stanford University; Ramki Gummadi, ; Miguel Lazaro-Gredilla, Vicarious; Dale Schuurmans, ; Stefano Ermon, Stanford
257 Best arm identification in multi-armed bandits with delayed and partial feedback Aditya Grover*, Stanford University; Todor Markov, ; Stefano Ermon, Stanford
267 Fully adaptive algorithm for pure exploration in linear bandits Liyuan Xu*, The University of Tokyo / RIKEN; Junya Honda, University of Tokyo / RIKEN; Masashi Sugiyama, RIKEN / The University of Tokyo
272 Contextual Bandits with Stochastic Experts Rajat Sen*, University of Texas at Austin; Karthikeyan Shanmugam, IBM; Sanjay Shakkottai, University of Texas at Austin
277 Human Interaction with Recommendation Systems Sven Schmit*, Stanford University; Carlos Riquelme,
281 Community Detection in Hypergraphs: Optimal Statistical Limit and Efficient Algorithms I Chien, UIUC; Chung-Yi Lin*, National Taiwan University; I-Hsiang Wang, National Taiwan University
294 Smooth and Sparse Optimal Transport Mathieu Blondel*, NTT; Vivien Seguy, Kyoto University; Antoine Rolet, Kyoto University
296 Robust Maximization of Non-Submodular Objectives Ilija Bogunovic*, ; Junyao Zhao, ETH Zürich; Volkan Cevher, EPFL
298 Cause-Effect Inference by Comparing Regression Errors Patrick Bloebaum*, Osaka University; Dominik Janzing, MPI for Intelligent Systems; Takashi Washio, ; Shohei Shimizu, ; Bernhard Schoelkopf, MPI for Intelligent Systems
299 Tree-based Bayesian Mixture Model for Competing Risks Alexis Bellot*, University of Oxford; Mihaela Van der Schaar, University of Oxford
301 Actor-Critic Fictitious Play in Simultaneous Move Multistage Games Julien Perolat*, DeepMind; Bilal Piot, DeepMind; Olivier Pietquin, DeepMind
307 Random Subspace with Trees for Feature Selection Under Memory Constraints Antonio Sutera*, ULiège; Célia Châtel, Aix-Marseille University; Gilles Louppe, ULiège; Louis Wehenkel, ; Pierre Geurts, ULiège
308 Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information Jakob Runge*, German Aerospace Agency
310 Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures Tomi Silander*, Naverlabs Europe; Janne Leppä-aho, ; Elias Jääsaari, ; Teemu Roos,
311 Convex optimization over intersection of simple sets: improved convergence rate guarantees via exact penalty approach Achintya Kundu*, Indian Institute of Science; Francis Bach, Inria / ENS; Chiranjib Bhattacharya,
317 Variational Sequential Monte Carlo Christian Naesseth*, Linköping University; Scott Linderman, ; Rajesh Ranganath, Princeton; David Blei,
321 Statistically Efficient Estimation for Non-Smooth Probability Densities Masaaki Imaizumi*, ISM / RIKEN; Takanori Maehara, ; Yuichi Yoshida, National Institute of Informatics
324 SDCA-Powered Inexact Dual Augmented Lagrangian Method for Fast CRF Learning Xu Hu*, ENPC; Guillaume Obozinski,
325 Generalized Concomitant Multi-Task Lasso for sparse multimodal regression Mathurin Massias*, INRIA Saclay; Olivier Fercoq, LTCI - Télécom ParisTech - Université Paris Saclay; Alexandre Gramfort, INRIA Saclay; Joseph Salmon, LTCI - Télécom ParisTech - Université Paris Saclay
328 Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models Atsushi Nitanda*, The University of Tokyo; Taiji Suzuki, The University of Tokyo
332 Statistical Sparse Online Regression: A Diffusion Approximation Perspective Junchi Li*, Princeton University; Qiang Sun, Princeton University; Jianqing Fan, Princeton University
338 Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization Fanhua Shang*, The Chinese University of Hong Kong; Yuanyuan Liu, The Chinese University of Hong Kong; Kaiwen Zhou, The Chinese University of Hong Kong; James Cheng, The Chinese University of Hong Kong; Kelvin Kai Wing Ng, The Chinese University of Hong Kong; Yuichi Yoshida, National Institute of Informatics
340 Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs Lawrence Murray*, Uppsala University; Daniel Lundén, ; Jan Kudlicka, ; David Broman, ; Thomas Schön,
342 Learning to Round for Discrete Labeling Problems Pritish Mohapatra*, IIIT, Hyderabad; Jawahar C.V., IIIT Hyderabad; M. Pawan Kumar, University of Oxford
350 Approximate ranking from pairwise comparisons Reinhard Heckel*, Rice University; Max Simchowitz, UC Berkeley; Kannan Ramchandran, UC Berkeley; Martin Wainwright, UC Berkeley
352 Semi-Supervised Prediction-Constrained Topic Models Michael Hughes*, Harvard University; John Hope, University of California, Irvine; Leah Weiner, Brown University; Thomas McCoy, Massachusetts General Hospital; Roy Perlis, Massachusetts General Hospital; Erik Sudderth, University of California, Irvine; Finale Doshi-Velez, Harvard
354 A Stochastic Differential Equation Framework for Guiding Online User Activities in Closed Loop Yichen Wang*, Gatech; Evangelos Theodorou, ; Le Song, Georgia Tech
358 Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms Pan Xu*, University of Virginia; Tianhao Wang, ; Quanquan Gu, University of Virginia
367 A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery Xiao Zhang*, University of Virginia; Lingxiao Wang, University of Virginia; Quanquan Gu, University of Virginia
370 Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling Hongyi Ding*, The University of Tokyo; Mohammad Khan, ; Issei Sato, The University of Tokyo / RIKEN; Masashi Sugiyama, RIKEN / The University of Tokyo
371 Factor Analysis on a Graph Masayuki Karasuyama*, ; Hiroshi Mamitsuka, Kyoto University / Aalto University
375 Crowdclustering with Partition Labels Junxiang Chen*, Northeastern University; Yale Chang, Northeastern University; Peter Castaldi, Brigham and Women’s Hospital; Michael Cho, Brigham and Women’s Hospital; Brian Hobbs, Brigham and Women’s Hospital; Jennifer Dy, North Eastern
378 Learning Structural Weight Uncertainty with Stein Gradient Flows Ruiyi Zhang, Duke University; Chunyuan Li*, Duke University; Changyou Chen, SUNY Buffalo; Lawrence Carin, Duke
382 Towards Memory-Friendly Deterministic Incremental Gradient Method Jiahao Xie*, Zhejiang University; Hui Qian, Zhejiang University; Zebang Shen, Zhejiang University; Chao Zhang, Zhejiang University
383 Alpha-expansion is Exact on Stable Instances Hunter Lang*, MIT; David Sontag, MIT; Aravindan Vijayaraghavan, Northwestern University
384 Bayesian Approaches to Distribution Regression Ho Chung Leon Law*, University Of Oxford; Dougal Sutherland, Gatsby unit, UCL; Dino Sejdinovic, University of Oxford; Seth Flaxman, Imperial College London
386 Submodularity on Hypergraphs: From Sets to Sequences Marko Mitrovic*, Yale University; Moran Feldman, Open University of Israel; Andreas Krause, ETH Zurich; Amin Karbasi, Yale
389 Provable Estimation of the Number of Blocks in Block Models BOWEI YAN*, UNIVERSITY OF TEXAS AT AUSTIN; Purnamrita Sarkar, University of Texas at Austin; Xiuyuan Cheng, Duke University
391 Differentially Private Regression with Gaussian Processes Michael Smith*, University of Sheffield; Mauricio Álvarez, University of Sheffield; Max Zwiessele, University of Sheffield; Neil Lawrence, University of Sheffield
394 Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems Sai Praneeth Reddy Karimireddy, EPFL; Sebastian Stich, EPFL; Martin Jaggi*, EPFL
407 VAE with a VampPrior Jakub Tomczak*, University of Amsterdam; Max Welling, University of Amsterdam
408 Structured Factored Inference for Probabilistic Programming Avi Pfeffer, Charles River Analytics; Brian Ruttenberg, Charles River Analytics; William Kretschmer, MIT; Alison OConnor*, Charles River Analytics
410 A Generic Approach for Escaping Saddle points Sashank Reddi, Google; Manzil Zaheer*, Carnegie Mellon University; Suvrit Sra, MIT; Barnabas Poczos, Carnegie Mellon University; Francis Bach, Inria / ENS; Ruslan Salakhutdinov, Carnegie Mellon University; Alex Smola, Amazon
411 Policy Evaluation and Optimization with Continuous Treatments Nathan Kallus*, ; Angela Zhou, Cornell ORIE
412 Multiphase MCMC Sampling for Parameter Inference in Nonlinear Ordinary Differential Equations Alan Lazarus*, University of Glagsow; Dirk Husmeier, Glasgow; Theodore Papamarkou, Mathematics & Statistics, University of Glasgow
414 Why adaptively collected data have negative bias and how to correct for it. Xinkun Nie*, Stanford University; Xiaoying Tian, Stanford University; Jonathan Taylor, Stanford University; James Zou, Stanford University
425 Sparse Linear Isotonic Models Sheng Chen*, University of Minnesota; Arindam Banerjee, University of Minnesota
431 Robustness of classifiers to uniform \ell_p and Gaussian noise Jean-Yves Franceschi, Ecole Normale Supérieure Lyon; Alhussein Fawzi*, UCLA; Omar Fawzi,
436 Nested CRP with Hawkes-Gaussian Processes Xi Tan*, Purdue University; Vinayak Rao, Purdue; Jennifer Neville, Purdue University
441 Sketching for Kronecker Product Regression and P-splines Huaian Diao, Northeast Normal University ; Zhao Song, UT-Austin; Wen Sun*, Carnegie Mellon University; David Woodruff, Carnegie Mellon University
442 Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models Ardavan Saeedi*, ; Matthew Hoffman, Google; Matthew Hoffman, Google; Stephen DiVerdi, Adobe; Asma Ghandeharioun, MIT; Matthew Johnson, Google Brain; Ryan Adams, Princeton
444 Cheap Checking for Cloud Computing: Statistical Analysis via Annotated Data Streams Chris Hickey*, University of Warwick; Graham Cormode,
447 Reconstruction Risk of Convolutional Sparse Dictionary Learning Shashank Singh*, ; Barnabas Poczos, Carnegie Mellon University; Jian Ma, Carnegie Mellon University
448 Kernel Conditional Exponential Family Michael Arbel*, Gatsby unit, UCL; Arthur Gretton, Gatsby unit, UCL
451 Linear Stochastic Approximation: Constant Step-Size and Iterate Averaging Chandrashekar Lakshmi-Narayanan*, Indian Institute of Science; Csaba Szepesvari,
452 Stochastic Zeroth-order Optimization in High Dimensions Yining Wang*, Carnegie Mellon University; Simon Du, ; Sivaraman Balakrishnan, Carnegie Mellon University; Aarti Singh, Carnegie Mellon University
459 Teacher Improves Learning by Selecting a Training Subset Philippe Rigollet, Massachusetts Institute of Technology; Robert Nowak, ; Xiaojin Zhu*, University of Wisconsin-Madison; Xuezhou Zhang, University of Wisconsin-Madison; Yuzhe Ma, Univ. of Wisconsin-Madison
460 Communication-Avoiding Optimization Methods for Massive-Scale Graphical Model Structure Learning Penporn Koanantakool*, UC Berkeley; Alnur Ali, Carnegie Mellon University; Ariful Azad, Lawrence Berkeley National Laboratory; Aydin Buluc, Lawrence Berkeley National Laboratory; Dmitriy Morozov, Lawrence Berkeley National Laboratory; Sang-Yun Oh, University of California, Santa Barbara; Leonid Oliker, Lawrence Berkeley National Laboratory; Katherine Yelick, Lawrence Berkeley National Laboratory
462 Robust Vertex Enumeration for Convex Hulls in High Dimensions Pranjal Awasthi*, Rutgers University; Bahman Kalantari, ; Yikai Zhang, Rutgers
468 Fast generalization error bound of deep learning from a kernel perspective Taiji Suzuki*, The University of Tokyo
471 Product Kernel Interpolation for Scalable Gaussian Processes Jacob Gardner*, Cornell University; Geoff Pleiss, Cornell University; Ruihan Wu, Tsinghua University; Kilian Weinberger, Cornell University; Andrew Wilson, Cornell University
472 Towards Provable Learning of Polynomial Neural Networks Using Low-Rank Matrix Estimation MOHAMMADREZA SOLTANI*, Iowa State University; Chinmay Hegde, Iowa State University
474 Scalable Generalized Dynamic Topic Models Patrick Jähnichen*, Humboldt-Universität zu Berlin; Florian Wenzel, Humboldt-Universität zu Berlin; Marius Kloft, Humboldt-Universität zu Berlin; Stephan Mandt, Disney Research
478 Bayesian Structure Learning for Dynamic Brain Connectivity Michael Andersen*, Aalto University; Oluwasanmi Koyejo, UIUC; Ole Winther, DTU; Lars Kai Hansen, Technical University of Denmark; Russell Poldrack, Stanford University
482 Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method Mark Eisen*, University of Pennsylvania; Aryan Mokhtari, University of California, Berkeley; Alejandro Ribeiro, University of Pennsylvania
483 Frank-Wolfe Splitting via Augmented Lagrangian Method Gauthier Gidel*, MILA; Fabian Pedregosa, UC Berkeley; Simon Lacoste-Julien, Montreal
487 Learning linear structural equation models in polynomial time and sample complexity Asish Ghoshal*, Purdue University; Jean Honorio, Purdue
490 Convergence diagnostics for stochastic gradient descent Jerry Chee*, University of Chicago; Panos Toulis,
496 Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity Asish Ghoshal*, Purdue University; Jean Honorio, Purdue
499 Nonparametric Sharpe Ratio Function Estimation in Heteroscedastic Regression Models via Convex Optimization Seung-Jean Kim, ; Johan Lim, Seoul National University; Joong-Ho Won*, Seoul National University
500 Stochastic algorithms for entropy-regularized optimal transport problems Brahim Khalil Abid*, Ecole polytechnique; Robert Gower, Telecom Paristech
502 Plug-in Estimators for Conditional Expectations and Probabilities Steffen Grunewalder*, Lancaster University
503 Factorized Recurrent Neural Architectures for Longer Range Dependence Francois Belletti*, UC Berkeley; Alex Beutel, Google Inc.; Sagar Jain, Google Inc.; Ed Chi, Google Inc.
504 On the Statistical Efficiency of Compositional Nonparametric Prediction Yixi Xu*, Purdue University; Jean Honorio, Purdue; Xiao Wang, Purdue University
509 Metrics for Deep Generative Models Nutan Chen*, Volkswagen Group; Richard Kurle, ; Alexej Klushyn, ; Justin Bayer, ; Xueyan Jiang, ; Patrick van der Smagt,
510 Combinatorial Penalties: Which structures are preserved by convex relaxations? Marwa El Halabi*, EPFL; Francis Bach, Inria / ENS; Volkan Cevher, EPFL
513 Generalized Binary Search For Split-Neighborly Problems Stephen Mussmann*, Stanford University; Percy Liang, Stanford University
518 Intersection-Validation: A Method for Evaluating Structure Learning without Ground Truth Jussi Viinikka*, ; Ralf Eggeling, University of Helsinki; Mikko Koivisto,
522 On Statistical Optimality of Variational Bayes Anirban Bhattacharya, Texas A&M University; Debdeep Pati*, Texas A&M University; Yun Yang,
524 Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems Jason Ge*, Princeton University; Zhaoran Wang, ; Mengdi Wang, ; Han Liu, Princeton
525 Online Regression with Partial Information: Generalization and Linear Projection Shinji Ito*, NEC Coorporation; Daisuke Hatano, ; Hanna Sumita, ; Akihiro Yabe, ; Takuro Fukunaga, ; Naonori Kakimura, ; Ken-Ichi Kawarabayashi,
526 Learning Generative Models with Sinkhorn Divergences Aude Genevay*, Université Paris Dauphine; Gabriel Peyre, ; Marco Cuturi, ENSAE/CREST
532 Reparameterizing the Birkhoff Polytope for Variational Permutation Inference Scott Linderman, ; Gonzalo Mena*, Columbia University; Hal Cooper, Columbia University; Liam Paninski, Columbia University; John Cunningham, Columbia University
534 Achieving the time of 1-NN, but the accuracy of k-NN Lirong Xue*, Princeton University; Samory Kpotufe, Princeton University
535 Efficient Weight Learning in High-Dimensional Untied MLNs Khan Mohammad Al Farabi*, The University of Memphis; Somdeb Sarkhel, Adobe Research; Deepak Venugopal, University of Memphis
536 Consistent Algorithms for Classification under Complex Losses and Constraints Harikrishna Narasimhan*, Harvard University
539 Solving lp-norm regularization with tensor kernels Saverio Salzo*, Istituto Italiano di Tecnologi; Lorenzo Rosasco, University of Genova & MIT; Johan Suykens,
546 Weighted Tensor Decomposition for Learning Latent Variables with Partial Data Omer Gottesman*, Harvard University; Weiwei Pan, ; Finale Doshi-Velez, Harvard
547 Multi-objective Contextual Bandit Problem with Similarity Information Eralp Turgay, Bilkent University; Doruk Oner, Bilkent University; Cem Tekin*, Bilkent University
549 Turing: Composable inference for probabilistic programming Hong Ge*, University of Cambridge; Kai Xu, University of Edinburgh; Zoubin Ghahramani, University of Cambridge
550 Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure Beilun Wang*, University of Virginia; arshdeep Sekhon, University of Virginia; Yanjun Qi,
551 Data-Efficient Reinforcement Learning with \\Probabilistic Model Predictive Control Sanket Kamthe, Imperial College; Marc Deisenroth*, Imperial College London
557 Approximate Bayesian Computation with Kullback-Leibler Divergence as Data Discrepancy Bai Jiang*, Princeton University
561 Practical Bayesian optimization in the presence of outliers Ruben Martinez-Cantin*, ; Michael McCourt, SigOpt; Kevin Tee, SigOpt
563 Competing with Automata-based Expert Sequences Scott Yang*, D. E. Shaw & Co.; Mehryar Mohri,
564 Reducing Crowdsourcing to Graphon Estimation, Statistically Christina Lee*, Microsoft Research; Devavrat Shah, MIT
567 Robust Locally-Linear Controllable Embedding Ershad Banijamali*, University of Waterloo; Rui Shu, Stanford University; mohammad Ghavamzadeh, DeepMind; Hung Bui, Adobe Research; Ali Ghodsi, University of Waterloo
569 Combinatorial Semi-Bandits with Knapsacks Karthik Abinav Sankararaman*, University of Maryland College; Aleksandrs Slivkins, Microsoft Research NYC
571 Structured Optimal Transport David Alvarez Melis*, MIT; Tommi Jaakkola, MIT; Stefanie Jegelka, MIT
578 Graphical Models for Non-Negative Data Using Generalized Score Matching Shiqing Yu*, University of Washington; Mathias Drton, University of Washington; Ali Shojaie, University of Washington
581 Asynchronous Doubly Stochastic Group Regularized Learning Bin Gu*, University of Pittsburgh; Zhouyuan Huo, ; Heng Huang, University of Pittsburgh
582 Convergence of Value Aggregation for Imitation Learning Ching-An Cheng*, Georgia Institute of Technology; Byron Boots,
594 Inference in Sparse Graphs with Pairwise Measurements and Side Information Dylan Foster*, Cornell University; Karthik Sridharan, Cornell University; Daniel Reichman, UC Berkeley
595 Parallel and Distributed MCMC via Shepherding Distributions Arkabandhu Chowdhury*, Rice University; Christopher Jermaine, Rice University
602 The Power Mean Laplacian for Multilayer Graph Clustering Pedro Mercado*, Saarland University; Antoine Gautier, Saarland University; Francesco Tudisco, University of Strathclyde; Matthias Hein, Saarland University
604 Adaptive Sampling for Clustered Ranking Sumeet Katariya*, Univ of Wisconsin-Madison; Lalit Jain, University of Michigan Ann Arbor; Nandana Sengupta, University of Chicago; James Evans, University of Chicago; Robert Nowak, University of Wisconsin-Madison
611 Comparison Based Learning from Weak Oracles Ehsan Kazemi*, Yale; Lin Chen, Yale University; Sanjoy Dasgupta, University of California San Diego; Amin Karbasi, Yale
613 The Binary Space Partitioning-Tree Process Xuhui Fan*, UNSW; Bin Li, Fudan University; Scott Sisson, University of New South Wales
614 On denoising noisy modulo 1 samples of a function Mihai Cucuringu*, University of Oxford and the Alan Turing Institute; Hemant Tyagi, Alan Turing Institute
616 Scalable Hash-Based Estimation of Divergence Measures Morteza Noshad Iranzad*, University of Michigan; Alfred Hero, University of Michigan
619 Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap Aryan Mokhtari*, UC Berkeley; Hamed Hassani, ; Amin Karbasi, Yale
620 Online Continuous Submodular Maximization Lin Chen*, Yale University; Hamed Hassani, ; Amin Karbasi, Yale
626 Efficient Bayesian Methods for Counting Processes in Partially Observable Environments Ferdian Jovan*, University of Birmingham; Jeremy Wyatt, University of Birmingham; Nick Hawes, University of Oxford
629 Matrix-normal models for fMRI analysis Michael Shvartsman*, Princeton University ; Narayanan Sundaram, Intel Corporation; Mikio Aoi, Princeton University; Adam Charles, Princeton University; Theodore Wilke, Intel Corporation; Jonathan Cohen, Princeton University
631 The emergence of spectral universality in deep networks Jeffrey Pennington*, ; Samuel Schoenholz, Google; Surya Ganguli, Google Brain
635 Spectral Algorithms for Computing Fair Support Vector Machines Mahbod Olfat*, UC Berkeley; Anil Aswani, UC Berkeley
636 Bayesian Multi-label Learning with Sparse Features and Labels He Zhao*, Monash University; Piyush Rai, IIT Kanpur; Lan Du, """Faculty of Information Technology, Monash University, Australia"""; Wray Buntine, Monash University
637 Nonparametric Bayesian sparse graph linear dynamical systems Rahi Kalantari, UT-Austin; Joydeep Ghosh, UT Austin; Mingyuan Zhou*, University of Texas at Austin
639 Proximity Variational Inference Jaan Altosaar*, Princeton University; Rajesh Ranganath, Princeton; David Blei,
641 Near-Optimal Machine Teaching via Explanatory Teaching Sets Yuxin Chen*, Caltech; Oisin Mac Aodha, Caltech; Shihan Su, Caltech; Pietro Perona, Caltech; Yisong Yue, Caltech
643 Learning Hidden Quantum Markov Models Siddarth Srinivasan*, Georgia Institute of Technolog; Geoff Gordon, Carnegie Mellon University; Byron Boots,
644 Labeled Graph Clustering via Projected Gradient Descent Shiau Hong Lim*, IBM Research; Gregory Calvez,
646 Gradient Diversity: a Key Ingredient for Scalable Distributed Learning Dong Yin*, UC Berkeley; Ashwin Pananjady, UC Berkeley; Max Lam, Stanford University; Dimitris Papailiopoulos, ; Kannan Ramchandran, UC Berkeley; Peter Bartlett, UC Berkeley
648 HONES: A Fast and Tuning-free Homotopy Method For Online Newton Step Yuting Ye*, UC Berkeley; LIhua Lei, UC Berkeley; Cheng Ju, UC Berkeley
649 Probability–Revealing Samples Krzysztof Onak*, IBM Research; Xiaorui Sun, Microsoft Research
656 Reducing optimization to repeated classification Tatsunori Hashimoto*, Stanford; Steve Yadlowsky, Stanford University; John Duchi,
661 Online Ensemble Multi-kernel Learning Adaptive to Non-stationary and Adversarial Environments Yanning Shen, ; Tianyi Chen*, University of Minnesota; Georgios Giannakis, University of Minnesota
665 A Unified Dynamic Approach to Sparse Model Selection Chendi Huang*, Peking University; Yuan Yao, Hongkong University of Science and Techonology
666 Bootstrapping EM via Power EM and Convergence in the Naive Bayes Model Costis Daskalakis, ; Christos Tzamos*, Microsoft Research; Manolis Zampetakis, MIT
669 Dimensionality Reduced $\ell^{0}$-Sparse Subspace Clustering Yingzhen Yang*, Snap Research

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