Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes

Ryan Adams, George Dahl, Iain Murray; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:1-9

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Gaussian Process Topic Models

Amrudin Agovic, Arindam Banerjee; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:10-19

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Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream

Amr Ahmed, Eric Xing; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:20-29

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Gibbs sampling in open-universe stochastic languages

Nimar Arora, Erik Sudderth, Rodrigo de Salvo Braz, Stuart Russell; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:30-39

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Compiling Possibilistic Networks : Alternative Approaches to Possibilistic Inference

Raouia Ayachi, Nahla Ben Amor, Salem Benferhat, Rolf Haenni; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:40-47

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Possibilistic Answer Set Programming Revisited

Kim Bauters, Steven Schockaert, Martine De Cock, Dirk Vermeir; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:48-55

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Three new sensitivity analysis methods for influence diagrams

Debarun Bhattacharjya, Ross Shachter; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:56-64

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Bayesian Rose Trees

Charles Blundell, Yee Whye Teh, Katherine Heller; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:65-72

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Probabilistic Similarity Logic

Matthias Broecheler, Lilyana Mihalkova, Lise Getoor; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:73-82

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Risk Sensitive Path Integral Control

Bart van den Broek, Wim Wiegerinck, Bert Kappen; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:83-90

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RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains

Emma Brunskill, Stuart Russell; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:91-100

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ALARMS: Alerting and Reasoning Management System for Next Generation Aircraft Hazards

Alan Carlin, Nathan Schurr, Janusz Marecki; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:101-108

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An Online Learning-based Framework for Tracking

Kamalika Chaudhuri, Yoav Freund, Daniel Hsu; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:109-116

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Super-Samples from Kernel Herding

Yutian Chen, Max Welling, Alex Smola; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:117-124

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Prediction with Advice of Unknown Number of Experts

Alexey Chernov, Vladimir Vovk; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:125-133

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Lifted Inference for Relational Continuous Models

Jaesik Choi, David Hill, Eyal Amir; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:134-142

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Distribution over Beliefs for Memory Bounded Dec-POMDP Planning

Gabriel Corona, François Charpillet; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:143-150

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Automated Planning in Repeated Adversarial Games

Enrique Munoz de Cote, Adam M. Sykulski, Archie Chapman, Nick Jennings; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:151-158

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Inferring deterministic causal relations

Povilas Daniusis, Dominik Janzing, Joris Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:159-166

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Inference-less Density Estimation using Copula Networks

Gal Elidan; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:167-175

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A Scalable Method for Solving High-Dimensional Continuous POMDPs Using Local Approximation

Tom Erez, William Smart; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:176-183

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Playing games against nature: optimal policies for renewable resource allocation

Stefano Ermon, Jon Conrad, Carla Gomes, Bart Selman; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:184-192

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Maximum likelihood fitting of acyclic directed mixed graphs to binary data

Robin Evans, Thomas Richardson; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:193-200

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Learning Game Representations from Data Using Rationality Constraints

Xi Alice Gao, Avi Pfeffer; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:201-208

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Real-Time Scheduling via Reinforcement Learning

Robert Glaubius, Terry Tidwell, Christopher Gill, William Smart; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:209-217

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Formula-Based Probabilistic Inference

Vibhav Gogate, Pedro Domingos; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:218-227

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Regularized Maximum Likelihood for Intrinsic Dimension Estimation

Mithun Das Gupta, Thomas Huang; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:228-235

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MDPs with Unawareness

Joseph Y. Halpern, Nan Rong, Ashutosh Saxena; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:236-243

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Intracluster Moves for Constrained Discrete-Space MCMC

Firas Hamze, Nando de Freitas; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:244-251

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Robust Metric Learning with Smooth Optimization

Kaizhu Huang, Rong Jin, Zenglin Xu, Cheng-Lin Liu; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:252-259

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The Hierarchical Dirichlet Process Hidden Semi-Markov Model

Matthew Johnson, Alan Willsky; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:260-267

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Combining Spatial and Telemetric Features for Learning Animal Movement Models

Berk Kapicioglu, Robert Schapire, Martin Wikelski, Tamara Broderick; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:268-275

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BEEM : Bucket Elimination with External Memory

Kalev Kask, Rina Dechter, Andrew Gelfand; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:276-284

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Causal Conclusions that Flip Repeatedly

Kevin Kelly, Conor Mayo-Wilson; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:285-292

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Bayesian exponential family projections for coupled data sources

Arto Klami, Seppo Virtanen, Samuel Kaski; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:293-300

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Anytime Planning for Decentralized POMDPs using Expectation Maximization

Akshat Kumar, Shlomo Zilberstein; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:301-308

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Solving Hybrid Influence Diagrams with Deterministic Variables

Yijing Li, Prakash Shenoy; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:309-318

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Approximating Higher-Order Distances Using Random Projections

Ping Li, Michael Mahoney, Yiyuan She; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:319-328

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Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost

Ping Li; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:329-338

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Negative Tree Reweighted Belief Propagation

Qiang Liu, Alexander Ihler; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:339-346

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GraphLab: A New Framework for Parallel Machine Learning

Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:347-356

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Parameter-Free Spectral Kernel Learning

Qi Mao, Ivor W. Tsang; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:357-364

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Dirichlet Process Mixtures of Generalized Mallows Models

Marina Meila, Harr Chen; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:365-374

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Parametric Return Density Estimation for Reinforcement Learning

Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:375-382

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A Delayed Column Generation Strategy for Exact k-Bounded MAP Inference in Markov Logic Networks

Mathias Niepert; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:383-390

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Comparative Analysis of Probabilistic Models for Activity Recognition with an Instrumented Walker

Farheen Omar, Mathieu Sinn, Jakub Truszkowski, Pascal Poupart, James Tung, Allan Caine; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:391-399

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Algorithms and Complexity Results for Exact Bayesian Structure Learning

Sebastian Ordyniak, Stefan Szeider; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:400-407

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The Cost of Troubleshooting Cost Clusters with Inside Information

Thorsten Ottosen, Finn Jensen; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:408-415

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On Measurement Bias in Causal Inference

Judea Pearl; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:416-423

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On a Class of Bias-Amplifying Variables that Endanger Effect Estimates

Judea Pearl; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:424-431

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Confounding Equivalence in Causal Inference

Judea Pearl, Azaria Paz; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:432-440

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A Family of Computationally Efficient and Simple Estimators for Unnormalized Statistical Models

Miika Pihlaja, Michael Gutmann, Aapo Hyvärinen; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:441-448

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Merging Knowledge Bases in Possibilistic Logic by Lexicographic Aggregation

Guilin Qi, Jianfeng Du, Weiru Liu, David Bell; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:449-456

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Sparse-posterior Gaussian Processes for general likelihoods

Alan Qi, Ahmed Abdel-Gawad, Thomas Minka; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:457-464

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Characterizing the set of coherent lower previsions with a finite number of constraints or vertices

Erik Quaeghebeur; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:465-472

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Understanding Sampling Style Adversarial Search Methods

Raghuram Ramanujan, Ashish Sabharwal, Bart Selman; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:473-482

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Irregular-Time Bayesian Networks

Michael Ramati, Yuval Shahar; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:483-490

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Inference by Minimizing Size, Divergence, or their Sum

Sebastian Riedel, David Smith, Andrew McCallum; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:491-498

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Convergent and Correct Message Passing Schemes for Optimization Problems over Graphical Models

Nicholas Ruozzi, Sekhar Tatikonda; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:499-499

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Exact and Approximate Inference in Associative Hierarchical Random Fields using Graph-Cuts

Chris Russell, Lubor Ladicky, Philip Torr, Pushmeet Kohli; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:500-507

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Dynamic programming in influence diagrams with decision circuits

Ross Shachter, Debarun Bhattacharjya; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:508-515

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Maximizing the Spread of Cascades Using Network Design

Daniel Sheldon, Bistra Dilkina, Adam Elmachtoub, Ryan Finseth, Ashish Sabharwal, Jon Conrad, Carla Gomes, David Shmoys, Will Allen, Ole Amundsen, William Vaughan; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:516-525

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On the Validity of Covariate Adjustment for Estimating Causal Effects

Ilya Shpitser, Tyler Vander Weele, James Robins; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:526-535

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Gaussian Process Structural Equation Models with Latent Variables

Ricardo Silva, Robert Gramacy; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:536-544

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Modeling Events with Cascades of Poisson Processes

Aleksandr Simma, Michael Jordan; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:545-554

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A Bayesian Matrix Factorization Model for Relational Data

Ajit Singh, Geoffrey Gordon; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:555-562

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Variance-Based Rewards for Approximate Bayesian Reinforcement Learning

Jonathan Sorg, Satinder Singh, Richard Lewis; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:563-570

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Identifying Causal Effects with Computer Algebra

Seth Sullivant, Luis David Garcia-Puente, Sarah Spielvogel; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:571-578

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Matrix Coherence and the Nystrom Method

Ameet Talwalkar, Afshin Rostamizadeh; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:579-586

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Bayesian Inference in Monte-Carlo Tree Search

Gerald Tesauro, VT Rajan, Richard Segal; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:587-595

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Bayesian Model Averaging Using the k-best Bayesian Network Structures

Jin Tian, Ru He, Lavanya Ram; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:596-604

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Learning networks determined by the ratio of prior and data

Maomi Ueno; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:605-612

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Online Semi-Supervised Learning on Quantized Graphs

Michal Valko, Branislav Kveton, Ling Huang, Daniel Ting; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:613-621

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Speeding up the binary Gaussian process classification

Jarno Vanhatalo, Aki Vehtari; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:622-630

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Efficient clustering with limited distance information

Konstantin Voevodski, Maria-Florina Balcan, Heiko Roeglin, Shang-Hua Teng, Yu Xia; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:631-639

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Learning Why Things Change: The Difference-Based Causality Learner

Mark Voortman, Denver Dash, Marek Druzdzel; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:640-649

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Primal View on Belief Propagation

Tomas Werner; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:650-656

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Truthful Feedback for Sanctioning Reputation Mechanisms

Jens Witkowski; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:657-664

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Rollout Sampling Policy Iteration for Decentralized POMDPs

Feng Wu, Shlomo Zilberstein, Xiaoping Chen; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:665-672

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Semi-supervised Learning by Modeling Multiple-Annotator Expertise

Yan Yan, Romer Rosales, Glenn Fung, Jennifer Dy; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:673-681

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Hybrid Generative/Discriminative Learning for Automatic Image Annotation

Shuang-Hong Yang, Jiang Bian, Hongyuan Zha; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:682-689

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Solving Multistage Influence Diagrams using Branch-and-Bound Search

Changhe Yuan, Xiaojian Wu, Eric Hansen; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:690-699

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A Convex Formulation for Learning Task Relationships in Multi-Task Learning

Yu Zhang, Dit-Yan Yeung; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:700-709

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Multi-Domain Collaborative Filtering

Yu Zhang, Bin Cao, Dit-Yan Yeung; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:710-717

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Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery

Kun Zhang, Bernhard Schölkopf, Dominik Janzing; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:718-725

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Learning Structural Changes of Gaussian Graphical Models in Controlled Experiments

Bai Zhang, Yue Wang; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:726-733

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Source separation and higher-order causal analysis of MEG and EEG

Kun Zhang, Aapo Hyvärinen; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:734-741

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Automatic Tuning of Interactive Perception Applications

Qian Zhu, Branislav Kveton, Lily Mummert, Padmanabhan Pillai; Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:742-750

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