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


Editors: Ryan P. Adams, Vibhav Gogate


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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