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