The 27th Uncertainty in Artificial Intelligence Conference: Preface

Fabio Cozman, Avi Pfeffer; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:1-17

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Graphical Models for Bandit Problems

Kareem Amin, Michael Kearns, Umar Syed; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:18-27

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Extended Lifted Inference with Joint Formulas

Udi Apsel, Ronen I. Brafman; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:28-35

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Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search

John Asmuth, Michael L. Littman; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:36-43

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Solving Cooperative Reliability Games

Yoram Bachrach, Reshef Meir, Michal Feldman, Moshe Tennenholtz; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:44-51

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Active Diagnosis via AUC Maximization: An Efficient Approach for Multiple Fault Identification in Large Scale, Noisy Networks

Gowtham Bellala, Jason Stanley, Clayton Scott, Suresh K. Bhavnani; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:52-59

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Semi-supervised Learning with Density Based Distances

Avleen S. Bijral, Nathan Ratliff, Nathan Srebro; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:60-67

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Deconvolution of mixing time series on a graph

Alexander W. Blocker, Edoardo M. Airoldi; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:68-88

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Factored Filtering of Continuous-Time Systems

E. Busra Celikkaya, Christian R. Shelton, William Lam; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:89-96

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Near-Optimal Target Learning With Stochastic Binary Signals

Mithun Chakraborty, Sanmay Das, Malik Magdon-Ismail; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:97-104

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Filtered Fictitious Play for Perturbed Observation Potential Games and Decentralised POMDPs

Archie C. Chapman, Simon A. Williamson, Nicholas R. Jennings; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:105-113

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A Framework for Optimizing Paper Matching

Laurent Charlin, Richard S. Zemel, Craig Boutilier; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:114-123

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A temporally abstracted Viterbi algorithm

Shaunak Chatterjee, Stuart Russell; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:124-132

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Smoothing Proximal Gradient Method for General Structured Sparse Learning

Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell, Eric P. Xing; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:133-142

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EDML: A Method for Learning Parameters in Bayesian Networks

Arthur Choi, Khaled S. Refaat, Adnan Darwiche; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:143-152

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Strictly Proper Mechanisms with Cooperating Players

SangIn Chun, Ross D. Shachter; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:153-162

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A Logical Characterization of Constraint-Based Causal Discovery

Tom Claassen, Tom Heskes; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:163-172

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Ensembles of Kernel Predictors

Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:173-180

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Bayesian network learning with cutting planes

James Cussens; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:181-188

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Active Learning for Developing Personalized Treatment

Kun Deng, Joelle Pineau, Susan A. Murphy; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:189-196

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Efficient Optimal Learning for Contextual Bandits

Miroslav Dudik, Daniel Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:197-216

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A Unifying Framework for Linearly Solvable Control

Krishnamurthy Dvijotham, Emanuel Todorov; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:217-224

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Boosting as a Product of Experts

Narayanan U. Edakunni, Gary Brown, Tim Kovacs; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:225-232

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PAC-Bayesian Policy Evaluation for Reinforcement Learning

Mahdi MIlani Fard, Joelle Pineau, Csaba Szepesvari; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:233-240

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On the Complexity of Decision Making in Possibilistic Decision Trees

Helene Fargier, Nahla Ben Amor, Wided Guezguez; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:241-248

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Inference in Probabilistic Logic Programs using Weighted CNF’s

Daan Fierens, Guy Van den Broeck, Ingo Thon, Bernd Gutmann, Luc De Raedt; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:249-258

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Efficient Inference in Markov Control Problems

Thomas Furmston, David Barber; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:259-267

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Dynamic consistency and decision making under vacuous belief

Phan H. Giang; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:268-275

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Hierarchical Affinity Propagation

Inmar Givoni, Clement Chung, Brendan J. Frey; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:276-284

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Approximation by Quantization

Vibhav Gogate, Pedro Domingos; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:285-293

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Probabilistic Theorem Proving

Vibhav Gogate, Pedro Domingos; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:294-303

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Generalized Fisher Score for Feature Selection

Quanquan Gu, Zhenhui Li, Jiawei Han; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:304-311

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Active Semi-Supervised Learning using Submodular Functions

Andrew Guillory, Jeff A. Bilmes; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:312-320

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Bregman divergence as general framework to estimate unnormalized statistical models

Michael Gutmann, Jun-ichiro Hirayama; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:321-328

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Reasoning about RoboCup Soccer Narratives

Hannaneh Hajishirzi, Julia Hockenmaier, Erik T. Mueller, Eyal Amir; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:329-338

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Suboptimality Bounds for Stochastic Shortest Path Problems

Eric A. Hansen; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:339-348

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Sequential Inference for Latent Force Models

Jouni Hartikainen, Simo Sarkka; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:349-356

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What Cannot be Learned with Bethe Approximations

Uri Heinemann, Amir Globerson; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:357-364

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Portfolio Allocation for Bayesian Optimization

Eric Brochu, Matthew W. Hoffman, Nando de Freitas; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:365-384

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Sum-Product Networks: A New Deep Architecture

Hoifung Poon, Pedro Domingos; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:385-394

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Lipschitz Parametrization of Probabilistic Graphical Models

Jean Honorio; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:395-402

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Efficient Probabilistic Inference with Partial Ranking Queries

Jonathan Huang, Ashish Kapoor, Carlos E. Guestrin; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:403-410

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Noisy-OR Models with Latent Confounding

Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:411-420

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Discovering causal structures in binary exclusive-or skew acyclic models

Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:421-430

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Detecting low-complexity unobserved causes

Dominik Janzing, Eleni Sgouritsa, Oliver Stegle, Jonas Peters, Bernhard Schoelkopf; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:431-439

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Online Importance Weight Aware Updates

Nikos Karampatziakis, John Langford; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:440-451

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Modeling Social Networks with Node Attributes using the Multiplicative Attribute Graph Model

Myunghwan Kim, Jure Leskovec; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:452-466

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Pitman-Yor Diffusion Trees

David A. Knowles, Zoubin Ghahramani; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:467-475

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Learning Determinantal Point Processes

Alex Kulesza, Ben Taskar; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:476-484

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Message-Passing Algorithms for Quadratic Programming Formulations of MAP Estimation

Akshat Kumar, Shlomo Zilberstein; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:485-492

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An Efficient Protocol for Negotiation over Combinatorial Domains with Incomplete Information

Minyi Li, Quoc Bao Vo, Ryszard Kowalczyk; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:493-501

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Noisy Search with Comparative Feedback

Shiau Hong Lim, Peter Auer; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:502-509

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Variational Algorithms for Marginal MAP

Qiang Liu, Alexander T. Ihler; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:510-519

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Classification of Sets using Restricted Boltzmann Machines

Jérôme Louradour, Hugo Larochelle; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:520-536

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Belief change with noisy sensing in the situation calculus

Jianbing Ma, Weiru Liu, Paul Miller; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:537-544

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Improving the Scalability of Optimal Bayesian Network Learning with External-Memory Frontier Breadth-First Branch and Bound Search

Brandon Malone, Changhe Yuan, Eric A. Hansen, Susan Bridges; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:545-554

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Order-of-Magnitude Influence Diagrams

Radu Marinescu, Nic Wilson; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:555-562

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Asymptotic Efficiency of Deterministic Estimators for Discrete Energy-Based Models: Ratio Matching and Pseudolikelihood

Benjamin Marlin, Nando de Freitas; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:563-571

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Reconstructing Pompeian Households

David Mimno; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:572-579

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Conditional Restricted Boltzmann Machines for Structured Output Prediction

Volodymyr Mnih, Hugo Larochelle, Geoffrey E. Hinton; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:580-588

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Compact Mathematical Programs For DEC-MDPs With Structured Agent Interactions

Hala Mostafa, Victor Lesser; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:589-596

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Fractional Moments on Bandit Problems

Ananda Narayanan B, Balaraman Ravindran; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:597-604

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Dynamic Mechanism Design for Markets with Strategic Resources

Swaprava Nath, Onno Zoeter, Yadati Narahari, Christopher R. Dance; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:605-612

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Multidimensional counting grids: Inferring word order from disordered bags of words

Nebojsa Jojic, Alessandro Perina; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:613-622

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Partial Order MCMC for Structure Discovery in Bayesian Networks

Teppo Niinimaki, Pekka Parviainen, Mikko Koivisto; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:623-630

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A Geometric Traversal Algorithm for Reward-Uncertain MDPs

Eunsoo Oh, Kee-Eung Kim; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:631-638

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Iterated risk measures for risk-sensitive Markov decision processes with discounted cost

Takayuki Osogami; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:639-646

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Price Updating in Combinatorial Prediction Markets with Bayesian Networks

David M. Pennock, Lirong Xia; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:647-654

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Identifiability of Causal Graphs using Functional Models

Jonas Peters, Joris Mooij, Dominik Janzing, Bernhard Schoelkopf; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:655-664

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Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions

Barnabas Poczos, Liang Xiong, Jeff Schneider; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:665-674

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Compressed Inference for Probabilistic Sequential Models

Gungor Polatkan, Oncel Tuzel; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:675-684

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Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks

Vinayak Rao, Yee Whye Teh; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:685-692

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New Probabilistic Bounds on Eigenvalues and Eigenvectors of Random Kernel Matrices

Nima Reyhani, Hideitsu Hino, Ricardo Vigario; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:693-700

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Online and Batch Learning Algorithms for Data with Missing Features

Afshin Rostamizadeh, Alekh Agarwal, Peter Bartlett; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:701-713

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Symbolic Dynamic Programming for Discrete and Continuous State MDPs

Scott Sanner, Karina Valdivia Delgado, Leliane Nunes de Barros; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:714-723

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Generalized Fast Approximate Energy Minimization via Graph Cuts: Alpha-Expansion Beta-Shrink Moves

Mark Schmidt (INRIA Paris - Rocquencourt), Karteek Alahari (INRIA Paris - Rocquencourt); Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:724-731

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An Efficient Algorithm for Computing Interventional Distributions in Latent Variable Causal Models

Ilya Shpitser, Thomas S. Richardson, James M. Robins; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:732-741

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Interpreting Graph Cuts as a Max-Product Algorithm

Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel, Brendan J. Frey; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:742-753

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Adjustment Criteria in Causal Diagrams: An Algorithmic Perspective

Johannes Textor, Maciej Liskiewicz; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:754-761

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Learning mixed graphical models from data with p larger than n

Inma Tur, Robert Castelo; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:762-770

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Robust learning Bayesian networks for prior belief

Maomi Ueno; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:771-780

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Distributed Anytime MAP Inference

Joop van de Ven, Fabio Ramos; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:781-789

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A Sequence of Relaxations Constraining Hidden Variable Models

Greg Ver Steeg, Aram Galstyan; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:790-799

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The Structure of Signals: Causal Interdependence Models for Games of Incomplete Information

Michael P. Wellman, Lu Hong, Scott E. Page; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:800-808

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Generalised Wishart Processes

Andrew Gordon Wilson, Zoubin Ghahramani; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:809-822

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Sparse matrix-variate Gaussian process blockmodels for network modeling

Feng Yan, Zenglin Xu, Yuan (Alan)Qi; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:823-830

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Hierarchical Maximum Margin Learning for Multi-Class Classification

Jian-Bo Yang, Ivor W. Tsang; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:831-838

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Planar Cycle Covering Graphs

Julian Yarkony, Alexander T. Ihler, Charless C. Fowlkes; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:839-847

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Tightening MRF Relaxations with Planar Subproblems

Julian Yarkony, Ragib Morshed, Alexander T. Ihler, Charless C. Fowlkes; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:848-855

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Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering

Yao-Liang Yu, Dale Schuurmans; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:856-866

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Measuring the Hardness of Stochastic Sampling on Bayesian Networks with Deterministic Causalities: the k-Test

Haohai Yu, Robert A. van Engelen; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:867-876

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Risk Bounds for Infinitely Divisible Distribution

Chao Zhang, Dacheng Tao; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:877-884

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Kernel-based Conditional Independence Test and Application in Causal Discovery

Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schoelkopf; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:885-894

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Smoothing Multivariate Performance Measures

Xinhua Zhang, Ankan Saha, S. V.N. Vishwanatan; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:895-902

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Belief Propagation by Message Passing in Junction Trees: Computing Each Message Faster Using GPU Parallelization

Lu Zheng, Ole Mengshoel, Jike Chong; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:903-911

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Sparse Topical Coding

Jun Zhu, Eric P. Xing; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:912-919

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Testing whether linear equations are causal: A free probability theory approach

Jakob Zscheischler, Dominik Janzing, Kun Zhang; Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:920-927

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