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Volume 115: Uncertainty in Artificial Intelligence, 22-25 July 2019, Tel Aviv, Israel

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Editors: Ryan P. Adams, Vibhav Gogate

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The 35th Uncertainty in Artificial Intelligence Conference: Preface

Ryan Adams,  Vibhav Gogate ; PMLR 115:1-17

Personalized Peer Truth Serum for Eliciting Multi-Attribute Personal Data

Naman Goel,  Boi Faltings ; PMLR 115:18-27

Conditional Expectation Propagation

Zheng Wang,  Shandian Zhe ; PMLR 115:28-37

A Sparse Representation-Based Approach to Linear Regression with Partially Shuffled Labels

Martin Slawski,  Mostafa Rahmani,  Ping Li ; PMLR 115:38-48

On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don’t Worry About its Nonsmooth Loss Function

Xinguo Li,  Haoming Jiang,  Jarvis Haupt,  Raman Arora,  Han Liu,  Mingyi Hong,  Tuo Zhao ; PMLR 115:49-59

Correlated Learning for Aggregation Systems

Tanvi Verma,  Pradeep Varakantham ; PMLR 115:60-70

Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias

Patrick Forré,  Joris M. Mooij ; PMLR 115:71-80

Variational Regret Bounds for Reinforcement Learning

Ronald Ortner,  Pratik Gajane,  Peter Auer ; PMLR 115:81-90

Recommendation from Raw Data with Adaptive Compound Poisson Factorization

Olivier Gouvert,  Thomas Oberlin,  Cédric Févotte ; PMLR 115:91-101

One-Shot Marginal MAP Inference in Markov Random Fields

Hao Xiong,  Yuanzhen Guo,  Yibo Yang,  Nicholas Ruozzi ; PMLR 115:102-112

Truly Proximal Policy Optimization

Yuhui Wang,  Hao He,  Xiaoyang Tan ; PMLR 115:113-122

Learning Factored Markov Decision Processes with Unawareness

Craig Innes,  Alex Lascarides ; PMLR 115:123-133

Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions

Tim Pearce,  Russell Tsuchida,  Mohamed Zaki,  Alexandra Brintrup,  Andy Neely ; PMLR 115:134-144

Countdown Regression: Sharp and Calibrated Survival Predictions

Anand Avati,  Tony Duan,  Sharon Zhou,  Kenneth Jung,  Nigam H. Shah,  Andrew Y. Ng ; PMLR 115:145-155

Reducing Exploration of Dying Arms in Mortal Bandits

Stefano Tracà,  Cynthia Rudin,  Weiyu Yan ; PMLR 115:156-163

Comparing EM with GD in Mixture Models of Two Components

Guojun Zhang,  Pascal Poupart,  George Trimponias ; PMLR 115:164-174

Efficient Search-Based Weighted Model Integration

Zhe Zeng,  Guy Van den Broeck ; PMLR 115:175-185

Causal Discovery with General Non-Linear Relationships using Non-Linear ICA

Ricardo Pio Monti,  Kun Zhang,  Aapo Hyvärinen ; PMLR 115:186-195

BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback

Chang Li,  Branislav Kveton,  Tor Lattimore,  Ilya Markov,  Maarten de Rijke,  Csaba Szepesvári,  Masrour Zoghi ; PMLR 115:196-206

Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory

Philipp Geiger,  Michel Besserve,  Justus Winkelmann,  Claudius Proissl,  Bernhard Schölkopf ; PMLR 115:207-216

The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA

Luigi Gresele,  Paul K. Rubenstein,  Arash Mehrjou,  Francesco Locatello,  Bernhard Schölkopf ; PMLR 115:217-227

Random Clique Covers for Graphs with Local Density and Global Sparsity

Sinead A. Williamson,  Mauricio Tec ; PMLR 115:228-238

Randomized Iterative Algorithms for Fisher Discriminant Analysis

Agniva Chowdhury,  Jiasen Yang,  Petros Drineas ; PMLR 115:239-249

Dynamic Trip-Vehicle Dispatch with Scheduled and On-Demand Requests

Taoan Huang,  Bohui Fang,  Xiaohui Bei,  Fei Fang ; PMLR 115:250-260

Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation

Cong Xie,  Oluwasanmi Koyejo,  Indranil Gupta ; PMLR 115:261-270

Adaptive Hashing for Model Counting

Jonathan Kuck,  Tri Dao,  Shengjia Zhao,  Burak Bartan,  Ashish Sabharwal,  Stefano Ermon ; PMLR 115:271-280

Towards a Better Understanding and Regularization of GAN Training Dynamics

Weili Nie,  Ankit B. Patel ; PMLR 115:281-291

Domain Generalization via Multidomain Discriminant Analysis

Shoubo Hu,  Kun Zhang,  Zhitang Chen,  Laiwan Chan ; PMLR 115:292-302

Efficient Planning Under Uncertainty with Incremental Refinement

Juan Carlos Saborío,  Joachim Hertzberg ; PMLR 115:303-312

Cubic Regularization with Momentum for Nonconvex Optimization

Zhe Wang,  Yi Zhou,  Yingbin Liang,  Guanghui Lan ; PMLR 115:313-322

Stability of Linear Structural Equation Models of Causal Inference

Karthik Abhinav Sankararaman,  Anand Louis,  Navin Goyal ; PMLR 115:323-333

Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning

Robert Peharz,  Antonio Vergari,  Karl Stelzner,  Alejandro Molina,  Xiaoting Shao,  Martin Trapp,  Kristian Kersting,  Zoubin Ghahramani ; PMLR 115:334-344

Towards Robust Relational Causal Discovery

Sanghack Lee,  Vasant Honavar ; PMLR 115:345-355

The Role of Memory in Stochastic Optimization

Antonio Orvieto,  Jonas Kohler,  Aurelien Lucchi ; PMLR 115:356-366

Random Search and Reproducibility for Neural Architecture Search

Liam Li,  Ameet Talwalkar ; PMLR 115:367-377

Joint Nonparametric Precision Matrix Estimation with Confounding

Sinong Geng,  Mladen Kolar,  Oluwasanmi Koyejo ; PMLR 115:378-388

General Identifiability with Arbitrary Surrogate Experiments

Sanghack Lee,  Juan D. Correa,  Elias Bareinboim ; PMLR 115:389-398

Differentiable Probabilistic Models of Scientific Imaging with the Fourier Slice Theorem

Karen Ullrich,  Rianne van den Berg,  Marcus Brubaker,  David Fleet,  Max Welling ; PMLR 115:399-411

Approximate Inference in Structured Instances with Noisy Categorical Observations

Alireza Heidari,  Ihab F. Ilyas,  Theodoros Rekatsinas ; PMLR 115:412-421

Randomized Value Functions via Multiplicative Normalizing Flows

Ahmed Touati,  Harsh Satija,  Joshua Romoff,  Joelle Pineau,  Pascal Vincent ; PMLR 115:422-432

A Fast Proximal Point Method for Computing Exact Wasserstein Distance

Yujia Xie,  Xiangfeng Wang,  Ruijia Wang,  Hongyuan Zha ; PMLR 115:433-453

Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks

Qi She,  Anqi Wu ; PMLR 115:454-464

Fisher-Bures Adversary Graph Convolutional Networks

Ke Sun,  Piotr Koniusz,  Zhen Wang ; PMLR 115:465-475

Epsilon-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning

Michael Gimelfarb,  Scott Sanner,  Chi-Guhn Lee ; PMLR 115:476-485

Periodic Kernel Approximation by Index Set Fourier Series Features

Anthony Tompkins,  Fabio Ramos ; PMLR 115:486-496

Efficient Neural Network Verification with Exactness Characterization

Krishnamurthy (Dj) Dvijotham,  Robert Stanforth,  Sven Gowal,  Chongli Qin,  Soham De,  Pushmeet Kohli ; PMLR 115:497-507

Augmenting and Tuning Knowledge Graph Embeddings

Robert Bamler,  Farnood Salehi,  Stephan Mandt ; PMLR 115:508-518

A Tighter Analysis of Randomised Policy Iteration

Meet Taraviya,  Shivaram Kalyanakrishnan ; PMLR 115:519-529

Perturbed-History Exploration in Stochastic Linear Bandits

Branislav Kveton,  Csaba Szepesvári,  Mohammad Ghavamzadeh,  Craig Boutilier ; PMLR 115:530-540

An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient

Pan Xu,  Felicia Gao,  Quanquan Gu ; PMLR 115:541-551

Deep Mixture of Experts via Shallow Embedding

Xin Wang,  Fisher Yu,  Lisa Dunlap,  Yi-An Ma,  Ruth Wang,  Azalia Mirhoseini,  Trevor Darrell,  Joseph E. Gonzalez ; PMLR 115:552-562

Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation

Manuel Haußmann,  Fred A. Hamprecht,  Melih Kandemir ; PMLR 115:563-573

Sliced Score Matching: A Scalable Approach to Density and Score Estimation

Yang Song,  Sahaj Garg,  Jiaxin Shi,  Stefano Ermon ; PMLR 115:574-584

Beyond Structural Causal Models: Causal Constraints Models

Tineke Blom,  Stephan Bongers,  Joris M. Mooij ; PMLR 115:585-594

Be Greedy: How Chromatic Number meets Regret Minimization in Graph Bandits

Shreyas S,  Aadirupa Saha,  Chiranjib Bhattacharyya ; PMLR 115:595-605

Approximate Causal Abstractions

Sander Beckers,  Frederick Eberhardt,  Joseph Y. Halpern ; PMLR 115:606-615

The Sensitivity of Counterfactual Fairness to Unmeasured Confounding

Niki Kilbertus,  Philip J. Ball,  Matt J. Kusner,  Adrian Weller,  Ricardo Silva ; PMLR 115:616-626

Belief Propagation: Accurate Marginals or Accurate Partition Function – Where is the Difference?

Christian Knoll,  Franz Pernkopf ; PMLR 115:627-636

Finding Minimal d-separators in Linear Time and Applications

Benito van der Zander,  Maciej Liśkiewicz ; PMLR 115:637-647

A Bayesian Approach to Robust Reinforcement Learning

Esther Derman,  Daniel Mankowitz,  Timothy Mann,  Shie Mannor ; PMLR 115:648-658

Adaptivity and Optimality: A Universal Algorithm for Online Convex Optimization

Guanghui Wang,  Shiyin Lu,  Lijun Zhang ; PMLR 115:659-668

Evacuate or Not? A POMDP Model of the Decision Making of Individuals in Hurricane Evacuation Zones

Adithya Raam Sankar,  Prashant Doshi,  Adam Goodie ; PMLR 115:669-678

Probabilistic Programming for Birth-Death Models of Evolution Using an Alive Particle Filter with Delayed Sampling

Jan Kudlicka,  Lawrence M. Murray,  Fredrik Ronquist,  Thomas B. Schön ; PMLR 115:679-689

Variational Sparse Coding

Francesco Tonolini,  Bjørn Sand Jensen,  Roderick Murray-Smith ; PMLR 115:690-700

Learning with Non-Convex Truncated Losses by SGD

Yi Xu,  Shenghuo Zhu,  Sen Yang,  Chi Zhang,  Rong Jin,  Tianbao Yang ; PMLR 115:701-711

Active Multi-Information Source Bayesian Quadrature

Alexandra Gessner,  Javier Gonzalez,  Maren Mahsereci ; PMLR 115:712-721

Cascading Linear Submodular Bandits: Accounting for Position Bias and Diversity in Online Learning to Rank

Gaurush Hiranandani,  Harvineet Singh,  Prakhar Gupta,  Iftikhar Ahamath Burhanuddin,  Zheng Wen,  Branislav Kveton ; PMLR 115:722-732

Sinkhorn AutoEncoders

Giorgio Patrini,  Rianne van den Berg,  Patrick Forré,  Marcello Carioni,  Samarth Bhargav,  Max Welling,  Tim Genewein,  Frank Nielsen ; PMLR 115:733-743

How to Exploit Structure while Solving Weighted Model Integration Problems

Samuel Kolb,  Pedro Zuidberg Dos Martires,  Luc De Raedt ; PMLR 115:744-754

Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation

Théo Galy-Fajou,  Florian Wenzel,  Christian Donner,  Manfred Opper ; PMLR 115:755-765

A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations

Biswajit Paria,  Kirthevasan Kandasamy,  Barnabás Póczos ; PMLR 115:766-776

Efficient Multitask Feature and Relationship Learning

Han Zhao,  Otilia Stretcu,  Alexander J. Smola,  Geoffrey J. Gordon ; PMLR 115:777-787

Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning

Jian Wu,  Saul Toscano-Palmerin,  Peter I. Frazier,  Andrew Gordon Wilson ; PMLR 115:788-798

Adaptively Truncating Backpropagation Through Time to Control Gradient Bias

Christopher Aicher,  Nicholas J. Foti,  Emily B. Fox ; PMLR 115:799-808

Sampling-free Uncertainty Estimation in Gated Recurrent Units with Applications to Normative Modeling in Neuroimaging

Seong Jae Hwang,  Ronak R. Mehta,  Hyunwoo J. Kim,  Sterling C. Johnson,  Vikas Singh ; PMLR 115:809-819

Online Factorization and Partition of Complex Networks by Random Walk

Lin Yang,  Zheng Yu,  Vladimir Braverman,  Tuo Zhao,  Mengdi Wang ; PMLR 115:820-830

On Densification for Minwise Hashing

Tung Mai,  Anup Rao,  Matt Kapilevich,  Ryan Rossi,  Yasin Abbasi-Yadkori,  Ritwik Sinha ; PMLR 115:831-840

N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification

Sami Abu-El-Haija,  Amol Kapoor,  Bryan Perozzi,  Joonseok Lee ; PMLR 115:841-851

Problem-dependent Regret Bounds for Online Learning with Feedback Graphs

Bingshan Hu,  Nishant A. Mehta,  Jianping Pan ; PMLR 115:852-861

Wasserstein Fair Classification

Ray Jiang,  Aldo Pacchiano,  Tom Stepleton,  Heinrich Jiang,  Silvia Chiappa ; PMLR 115:862-872

Variational Training for Large-Scale Noisy-OR Bayesian Networks

Geng Ji,  Dehua Cheng,  Huazhong Ning,  Changhe Yuan,  Hanning Zhou,  Liang Xiong,  Erik B. Sudderth ; PMLR 115:873-882

Guaranteed Scalable Learning of Latent Tree Models

Furong Huang,  Niranjan Uma Naresh,  Ioakeim Perros,  Robert Chen,  Jimeng Sun,  Anima Anandkumar ; PMLR 115:883-893

On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits

Roman Pogodin,  Tor Lattimore ; PMLR 115:894-904

Noise Contrastive Priors for Functional Uncertainty

Danijar Hafner,  Dustin Tran,  Timothy Lillicrap,  Alex Irpan,  James Davidson ; PMLR 115:905-914

Fake It Till You Make It: Learning-Compatible Performance Support

Jonathan Bragg,  Emma Brunskill ; PMLR 115:915-924

Literal or Pedagogic Human? Analyzing Human Model Misspecification in Objective Learning

Smitha Milli,  Anca D. Dragan ; PMLR 115:925-934

Convergence Analysis of Gradient-Based Learning in Continuous Games

Benjamin Chasnov,  Lillian Ratliff,  Eric Mazumdar,  Samuel Burden ; PMLR 115:935-944

End-to-end Training of Deep Probabilistic CCA on Paired Biomedical Observations

Gregory Gundersen,  Bianca Dumitrascu,  Jordan T. Ash,  Barbara E. Engelhardt ; PMLR 115:945-955

Approximate Relative Value Learning for Average-reward Continuous State MDPs

Hiteshi Sharma,  Mehdi Jafarnia-Jahromi,  Rahul Jain ; PMLR 115:956-964

Exact Sampling of Directed Acyclic Graphs from Modular Distributions

Topi Talvitie,  Aleksis Vuoksenmaa,  Mikko Koivisto ; PMLR 115:965-974

Intervening on Network Ties

Eli Sherman,  Ilya Shpitser ; PMLR 115:975-984

Generating and Sampling Orbits for Lifted Probabilistic Inference

Steven Holtzen,  Todd Millstein,  Guy Van den Broeck ; PMLR 115:985-994

Real-Time Robotic Search using Structural Spatial Point Processes

Olov Andersson,  Per Sidén,  Johan Dahlin,  Patrick Doherty,  Mattias Villani ; PMLR 115:995-1005

Social Reinforcement Learning to Combat Fake News Spread

Mahak Goindani,  Jennifer Neville ; PMLR 115:1006-1016

P3O: Policy-on Policy-off Policy Optimization

Rasool Fakoor,  Pratik Chaudhari,  Alexander J. Smola ; PMLR 115:1017-1027

Causal Inference Under Interference And Network Uncertainty

Rohit Bhattacharya,  Daniel Malinsky,  Ilya Shpitser ; PMLR 115:1028-1038

Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow

Tuan Anh Le,  Adam R. Kosiorek,  N. Siddharth,  Yee Whye Teh,  Frank Wood ; PMLR 115:1039-1049

Learnability for the Information Bottleneck

Tailin Wu,  Ian Fischer,  Isaac L. Chuang,  Max Tegmark ; PMLR 115:1050-1060

Learning Belief Representations for Imitation Learning in POMDPs

Tanmay Gangwani,  Joel Lehman,  Qiang Liu,  Jian Peng ; PMLR 115:1061-1071

Object Conditioning for Causal Inference

David Jensen,  Javier Burroni,  Matthew Rattigan ; PMLR 115:1072-1082

CCMI : Classifier based Conditional Mutual Information Estimation

Sudipto Mukherjee,  Himanshu Asnani,  Sreeram Kannan ; PMLR 115:1083-1093

Empirical Mechanism Design: Designing Mechanisms from Data

Enrique Areyan Viqueira,  Cyrus Cousins,  Yasser Mohammad,  Amy Greenwald ; PMLR 115:1094-1104

On the Relationship Between Satisfiability and Markov Decision Processes

Ricardo Salmon,  Pascal Poupart ; PMLR 115:1105-1115

Interpretable Almost Matching Exactly With Instrumental Variables

M. Usaid Awan,  Yameng Liu,  Marco Morucci,  Sudeepa Roy,  Cynthia Rudin,  Alexander Volfovsky ; PMLR 115:1116-1126

Low Frequency Adversarial Perturbation

Chuan Guo,  Jared S. Frank,  Kilian Q. Weinberger ; PMLR 115:1127-1137

Markov Logic Networks for Knowledge Base Completion: A Theoretical Analysis Under the MCAR Assumption

Ondřej Kuželka,  Jesse Davis ; PMLR 115:1138-1148

Identification In Missing Data Models Represented By Directed Acyclic Graphs

Rohit Bhattacharya,  Razieh Nabi,  Ilya Shpitser,  James M. Robins ; PMLR 115:1149-1158

A Weighted Mini-Bucket Bound for Solving Influence Diagram

Junkyu Lee,  Radu Marinescu,  Alexander Ihler,  Rina Dechter ; PMLR 115:1159-1168

Subspace Inference for Bayesian Deep Learning

Pavel Izmailov,  Wesley J. Maddox,  Polina Kirichenko,  Timur Garipov,  Dmitry Vetrov,  Andrew Gordon Wilson ; PMLR 115:1169-1179

Off-Policy Policy Gradient with Stationary Distribution Correction

Yao Liu,  Adith Swaminathan,  Alekh Agarwal,  Emma Brunskill ; PMLR 115:1180-1190

Co-training for Policy Learning

Jialin Song,  Ravi Lanka,  Yisong Yue,  Masahiro Ono ; PMLR 115:1191-1201

Variational Inference of Penalized Regression with Submodular Functions

Koh Takeuchi,  Yuichi Yoshida,  Yoshinobu Kawahara ; PMLR 115:1202-1211

Probability Distillation: A Caveat and Alternatives

Chin-Wei Huang,  Faruk Ahmed,  Kundan Kumar,  Alexandre Lacoste,  Aaron Courville ; PMLR 115:1212-1221

Bayesian Optimization with Binary Auxiliary Information

Yehong Zhang,  Zhongxiang Dai,  Bryan Kian Hsiang Low ; PMLR 115:1222-1232

On Open-Universe Causal Reasoning

Duligur Ibeling,  Thomas Icard ; PMLR 115:1233-1243

Embarrassingly Parallel MCMC using Deep Invertible Transformations

Diego Mesquita,  Paul Blomstedt,  Samuel Kaski ; PMLR 115:1244-1252

Fast Proximal Gradient Descent for A Class of Non-convex and Non-smooth Sparse Learning Problems

Yingzhen Yang,  Jiahui Yu ; PMLR 115:1253-1262

Block Neural Autoregressive Flow

Nicola De Cao,  Wilker Aziz,  Ivan Titov ; PMLR 115:1263-1273

Exclusivity Graph Approach to Instrumental Inequalities

Davide Poderini,  Rafael Chaves,  Iris Agresti,  Gonzalo Carvacho,  Fabio Sciarrino ; PMLR 115:1274-1283

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