The 29th Uncertainty in Artificial Intelligence Conference: Preface

Ann Nicholson, Padhraic Smyth; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:1-4

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Generative Multiple-Instance Learning Models For Quantitative Electromyography

Tameem Adel, Ruth Urner, Benn Smith, Daniel Stashuk, Dan Lizotte; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:5-14

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Active Sensing as Bayes-Optimal Sequential Decision Making

Sheeraz Ahmad, Angela Yu; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:15-24

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Lower Bounds for Exact Model Counting and Applications in Probabilistic Databases

Paul Beame, Jerry Li, Sudeepa Roy, Dan Suciu; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:25-34

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Properties of the Lovász-Bregman Divergence with applications to rank aggregation and clustering

Jeff Bilmes, Rishabh Iyer; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:35-44

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Hilbert Space Embeddings of Predictive State Representations

Byron Boots, Geoffrey Gordon, Arthur Gretton; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:45-54

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Automorphism Groups of Graphical Models and Lifted Variational Inference

Hung Bui, Tuyen Huynh, Sebasitan Riedel; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:55-64

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POMDPs under Probabilistic Semantics

Krishnendu Chatterjee, Martin Chmelík; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:65-74

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Learning Sparse Causal Models is not NP-hard

Tom Claassen, Joris Mooij, Tom Heskes; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:75-84

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Advances in Bayesian Network Learning using Integer Programming

James Cussens, Mark Bartlett; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:85-94

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Optimization With Parity Constraints: From Binary Codes to Discrete Integration

Stefano Ermon, Carla Gomes, Ashish Sabharwal, Bart Selman; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:95-104

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Bethe-ADMM for Tree Decomposition based Parallel MAP Inference

Qiang Fu, Huahua Wang, Arindam Banerjee; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:105-114

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Structured Message Passing

Vibhav Gogate, Pedro Domingos; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:115-124

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Approximation of Lorenz-Optimal Solutions in Multiobjective Markov Decision Processes

Judy Goldsmith, Josiah Hanna, Patrice Perny, Paul Weng; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:125-134

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Constrained Bayesian Inference for Low Rank Multitask Learning

Oluwasanmi Koyejo, Joydeep Ghosh; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:135-144

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Evaluating Anytime Algorithms for Learning Optimal Bayesian Networks

Brandon Malone, Changhe Yuan; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:145-154

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On the Complexity of Strong and Epistemic Credal Networks

Denis Maua, Cassio de Campos, Alessio Benavoli, Alessandro Antonucci; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:155-164

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Learning Periodic Human Behaviour Models from Sparse Data for Crowdsourcing Aid Delivery in Developing Countries

James McInerney, Alex Rogers, NIcholas Jennings; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:165-174

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Cyclic Causal Discovery from Continuous Equilibrium Data

Joris Mooij, Tom Heskes; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:175-183

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Treedy: A Heuristic for Counting and Sampling Subsets

Teppo Niinimäki, Mikko Koivisto; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:184-192

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Evaluating computational models of explanation using human judgments

Michael Pacer, Tania Lombrozo, Thomas Griffiths, Joseph Williams, Xi Chen; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:193-202

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Sparse Nested Markov models with Log-linear Parameters

Ilya Shpitser, Robin Evans, Thomas Richardson, James Robins; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:203-212

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Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality

Vikas Sindhwani, Ha Quang Minh, Aurelie Lozano; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:213-222

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Modeling Documents with Deep Boltzmann Machines

Nitish Srivastava, Ruslan Salakhutdinov, Geoffrey Hinton; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:223-231

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Bounded Approximate Symbolic Dynamic Programming for Hybrid MDPs

Luis Gustavo Vianna, Scott Sanner, Leliane de Barros; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:232-241

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On MAP Inference by MWSS on Perfect Graphs

Adrian Weller, Tony Jebara; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:242-251

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Active Learning with Expert Advice

Peilin ZHAO, Steven Hoi; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:252-261

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The Bregman Variational Dual-Tree Framework

Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:262-271

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Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction

Stephen Bach, Bert Huang, Ben London, Lise Getoor; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:272-281

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High-dimensional Joint Sparsity Random Effects Model for Multi-task Learning

Krishnakumar Balasubramanian, Kai Yu, Tong Zhang; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:282-291

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Reasoning about Probabilities in Dynamic Systems using Goal Regression

Vaishak Belle, Hector Levesque; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:292-301

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Probabilistic Conditional Preference Networks

Damien Bigot, Bruno Zanuttini, Helene Fargier, Jérôme Mengin; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:302-311

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Boosting in the presence of label noise

Jakramate Bootkrajang, Ata Kaban; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:312-321

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Scoring and Searching over Bayesian Networks with Causal and Associative Priors

Giorgos Borboudakis, Ioannis Tsamardinos; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:322-331

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SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure

Eliot Brenner, David Sontag; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:332-341

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Sample Complexity of Transfer Reinforcement Learning

Emma Brunskill, Lihong Li; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:342-351

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Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations

Jie Chen, Nannan Cao, Kian Hsiang Low, Colin Keng-Yan Tan, Patrick Jaillet; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:352-361

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Convex Relaxations of Bregman Divergence Clustering

Hao Cheng, Xinhua Zhang, Dale Schuurmans; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:362-371

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Qualitative possibilistic Mixed-Observable MDPs

Nicolas Drougard, Didier Dubois, Florent Teichteil-Königsbuch, Jean-Loup Farges; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:372-381

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Pushing the Envelope of Monte-Carlo Planning: Formal Guarantees Meet Practical Efficiency

Zohar Feldman, Carmel Domshlak; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:382-391

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Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens

Ravi Ganti, Alexander Gray; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:392-401

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Batch-iFDD for Representation Expansion in Large MDPs

Alborz Geramifard, Tom Walsh, Nicholas Roy, Jonathan How; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:402-411

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Multiple Instance Learning by Discriminative Training of Markov Networks

Hossein Hajimirsadeghi, jinling Li, Greg Mori, Tarek Sayed, Mohammad Zaki; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:412-421

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Unsupervised Learning of Noisy-OR Bayesian Networks

Yonatan Halpern, David Sontag; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:422-431

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Gaussian Processes for Big Data

James Hensman, Nicolo Fusi, Neil Lawrence; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:432-440

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Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models

Jean Honorio, Tommi Jaakkola; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:441-450

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Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure

Antti Hyttinen, Patrik Hoyer, Frederick Eberhardt, Matti Järvisalo; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:451-460

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Warped Mixtures for Nonparametric Cluster Shapes

Tomoharu Iwata, David Duvenaud, Zoubin Ghahramani; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:461-470

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Solving Limited-Memory Influence Diagrams Using Branch-and-Bound Search

Arindam Khaled, Changhe Yuan, Eric Hansen; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:471-480

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Collective Diffusion Over Networks: Models and Inference

Akshat Kumar, Dan Sheldon, Biplav Srivastava; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:481-490

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Normalized Online Learning

John Langford, Paul Mineiro, Stephane Ross; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:491-499

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Causal Transportability of Experiments on Controllable Subsets of Variables: z-Transportability

Sanghack Lee, Vasant Honavar; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:500-509

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A Sound and Complete Algorithm for Learning Causal Models from Relational Data

Marc Maier, Katerina Marazopoulou, David Arbour, David Jensen; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:510-519

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Learning Max-margin Tree Predictors

Ofer Meshi, Elad Eban, Gal Elidan, Amir Globerson; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:520-529

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Tighter Linear Program Relaxations for High Order Graphical Models

Elad Mezuman, Daniel Tarlow, Amir Globerson, Yair Weiss; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:530-539

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From Ordinary Differential Equations to Structural Causal Models: the deterministic case

Joris Mooij, Dominik Janzing, Bernhard Schoelkopf; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:540-548

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One-Class Support Measure Machines for Group Anomaly Detection

Krikamol Muandet, Bernhard Schoelkopf; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:549-558

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Finite-Time Analysis of Kernelised Contextual Bandits

Remi Munos, Michal Valko, Nathaniel Korda, Ilias Flaounas, Nelo Cristianini; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:559-568

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Structured Convex Optimization under Submodular Constraints

Kiyohito Nagano, Yoshinobu Kawahara; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:569-578

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Stochastic Rank Aggregation

Shuzi Niu, Yanyan Lan, Jiafeng Guo, Xueqi Cheng; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:579-588

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Pay or Play

Sigal Oren, Michael Schapira, Moshe Tennenholtz; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:589-598

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Solution Methods for Constrained Markov Decision Process with Continuous Probability Modulation

Marek Petrik, Dharmashankar Subramanian, Janusz Marecki; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:599-607

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The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models

Novi Quadrianto, Viktoriia Sharmanska, David A. Knowles, Zoubin Ghahramani; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:608-617

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Beyond Log-Supermodularity: Lower Bounds and the Bethe Partition Function

Nicholas Ruozzi; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:618-627

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Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders

Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schölkopf; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:628-637

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Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering

Amar Shah, Zoubin Ghahramani; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:638-647

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Preference Elicitation For General Random Utility Models

Hossein Azari Soufiani, David Parkes, Lirong Xia; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:648-657

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Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Models

Peter Spirtes; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:658-667

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Speedy Model Selection (SMS) for Copula Models

Yaniv Tenzer, Gal Elidan; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:668-677

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Probabilistic inverse reinforcement learning in unknown environments

Aristide Tossou, Christos Dimitrakakis; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:678-686

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Approximate Kalman Filter Q-Learning for Continuous State-Space MDPs

Charles Tripp, Ross Shachter; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:687-696

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Dynamic Blocking and Collapsing for Gibbs Sampling

Deepak Venugopal, Vibhav Gogate; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:697-706

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Integrating document clustering and topic modeling

Pengtao Xie, Eric Xing; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:707-716

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Bennett-type Generalization Bounds: Large-deviation Case and Faster Rate of Convergence

Chao Zhang, Jieping Ye; Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:717-725

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