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UAI 2010 Proceedings

The UAI 2010 Proceedings have also appeared in print, published by the AUAI Press. Please cite as: P. Grünwald and P. Spirtes (Editors). Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010). AUAI Press. ISBN 978-0-9749039-6-5.

Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes
Ryan Adams, George Dahl, Iain Murray

Gaussian Process Topic Models
Amrudin Agovic, Arindam Banerjee

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

Gibbs sampling in open-universe stochastic languages
Nimar Arora, Erik Sudderth, Rodrigo de Salvo Braz, Stuart Russell

Compiling Possibilistic Networks : Alternative Approaches to Possibilistic Inference
Raouia Ayachi, Nahla Ben Amor, Salem Benferhat , Rolf haenni

Possibilistic Answer Set Programming Revisited
Kim Bauters, Steven Schockaert, Martine De Cock, Dirk Vermeir

Three new sensitivity analysis methods for influence diagrams
Debarun Bhattacharjya, Ross Shachter

Bayesian Rose Trees
Charles Blundell, Yee Whye Teh, Katherine Heller

Probabilistic Similarity Logic
Matthias Broecheler, Lilyana Mihalkova, Lise Getoor

Risk Sensitive Path Integral Control
Bart van den Broek, Wim Wiegerinck, Bert Kappen

RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains
Emma Brunskill, Stuart Russell

ALARMS: Alerting and Reasoning Management System for Next Generation Aircraft Hazards
Alan Carlin, Nathan Schurr, Janusz Marecki

An Online Learning-based Framework for Tracking
Kamalika Chaudhuri, Yoav Freund, Daniel Hsu

Super-Samples from Kernel Herding
Yutian Chen, Max Welling, Alex Smola

Prediction with Advice of Unknown Number of Experts
Alexey Chernov, Vladimir Vovk

Lifted Inference for Relational Continuous Models
Jaesik Choi, David Hill, Eyal Amir

Distribution over Beliefs for Memory Bounded Dec-POMDP Planning
Gabriel Corona, François Charpillet

Automated Planning in Repeated Adversarial Games
Enrique Munoz de Cote, Adam M. Sykulski, Archie Chapman, Nick Jennings

Inferring deterministic causal relations
Povilas Daniusis, Dominik Janzing, Joris Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf

Inference-less Density Estimation using Copula Networks
Gal Elidan

A Scalable Method for Solving High-Dimensional Continuous POMDPs Using Local Approximation
Tom Erez, William Smart

Playing games against nature: optimal policies for renewable resource allocation
Stefano Ermon, Jon Conrad, Carla Gomes, Bart Selman

Maximum likelihood fitting of acyclic directed mixed graphs to binary data
Robin Evans, Thomas Richardson

Learning Game Representations from Data Using Rationality Constraints
Xi Alice Gao, Avi Pfeffer

Real-Time Scheduling via Reinforcement Learning
Robert Glaubius, Terry Tidwell, Christopher Gill, William Smart

Formula-Based Probabilistic Inference
Vibhav Gogate, Pedro Domingos

Regularized Maximum Likelihood for Intrinsic Dimension Estimation
Mithun Das Gupta, Thomas Huang

MDPs with Unawareness
Joseph Y. Halpern, Nan Rong, Ashutosh Saxena

Intracluster Moves for Constrained Discrete-Space MCMC
Firas Hamze, Nando de Freitas

Robust Metric Learning with Smooth Optimization
Kaizhu Huang, Rong Jin, Zenglin Xu, Cheng-Lin Liu

The Hierarchical Dirichlet Process Hidden Semi-Markov Model
Matthew Johnson, Alan Willsky

Combining Spatial and Telemetric Features for Learning Animal Movement Models
Berk Kapicioglu, Robert Schapire, Martin Wikelski, Tamara Broderick

BEEM : Bucket Elimination with External Memory
Kalev Kask, Rina Dechter, Andrew Gelfand

Causal Conclusions that Flip Repeatedly
Kevin Kelly, Conor Mayo-Wilson

Bayesian exponential family projections for coupled data sources
Arto Klami, Seppo Virtanen, Samuel Kaski

Anytime Planning for Decentralized POMDPs using Expectation Maximization
Akshat Kumar, Shlomo Zilberstein

Solving Hybrid Influence Diagrams with Deterministic Variables
Yijing Li, Prakash Shenoy

Approximating Higher-Order Distances Using Random Projections
Ping Li, Michael Mahoney, Yiyuan She

Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost
Ping Li

Negative Tree Reweighted Belief Propagation
Qiang Liu, Alexander Ihler

GraphLab: A New Framework for Parallel Machine Learning
Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin

Parameter-Free Spectral Kernel Learning
Qi Mao, Ivor W. Tsang

Dirichlet Process Mixtures of Generalized Mallows Models
Marina Meila, Harr Chen

Parametric Return Density Estimation for Reinforcement Learning
Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka

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

Comparative Analysis of Probabilistic Models for Activity Recognition with an Instrumented Walker
Farheen Omar, Mathieu Sinn, Jakub Truszkowski, Pascal Poupart, James Tung, Allan Caine

Algorithms and Complexity Results for Exact Bayesian Structure Learning
Sebastian Ordyniak, Stefan Szeider

The Cost of Troubleshooting Cost Clusters with Inside Information
Thorsten Ottosen, Finn Jensen

On Measurement Bias in Causal Inference
Judea Pearl

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

Confounding Equivalence in Causal Inference
Judea Pearl, Azaria Paz

A Family of Computationally Efficient and Simple Estimators for Unnormalized Statistical Models
Miika Pihlaja, Michael Gutmann, Aapo Hyvärinen

Merging Knowledge Bases in Possibilistic Logic by Lexicographic Aggregation
Guilin Qi, Jianfeng Du, Weiru Liu, David Bell

Sparse-posterior Gaussian Processes for general likelihoods
Alan Qi, Ahmed Abdel-Gawad, Thomas Minka

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

Understanding Sampling Style Adversarial Search Methods
Raghuram Ramanujan, Ashish Sabharwal, Bart Selman

Irregular-Time Bayesian Networks
Michael Ramati, Yuval Shahar

Inference by Minimizing Size, Divergence, or their Sum
Sebastian Riedel, David Smith, Andrew McCallum

Convergent and Correct Message Passing Schemes for Optimization Problems over Graphical Models
Nicholas Ruozzi, Sekhar Tatikonda

Exact and Approximate Inference in Associative Hierarchical Random Fields using Graph-Cuts
Chris Russell, Lubor Ladicky, Philip Torr, Pushmeet Kohli

Dynamic programming in influence diagrams with decision circuits
Ross Shachter, Debarun Bhattacharjya

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

On the Validity of Covariate Adjustment for Estimating Causal Effects
Ilya Shpitser, Tyler Vander Weele, James Robins

Gaussian Process Structural Equation Models with Latent Variables
Ricardo Silva, Robert Gramacy

Modeling Events with Cascades of Poisson Processes
Aleksandr Simma, Michael Jordan

A Bayesian Matrix Factorization Model for Relational Data
Ajit Singh, Geoffrey Gordon

Variance-Based Rewards for Approximate Bayesian Reinforcement Learning
Jonathan Sorg, Satinder Singh, Richard Lewis

Identifying Causal Effects with Computer Algebra
Seth Sullivant, Luis David Garcia-Puente, Sarah Spielvogel

Matrix Coherence and the Nystrom Method
Ameet Talwalkar, Afshin Rostamizadeh

Bayesian Inference in Monte-Carlo Tree Search
Gerald Tesauro, VT Rajan, Richard Segal

Bayesian Model Averaging Using the k-best Bayesian Network Structures
Jin Tian, Ru He, Lavanya Ram

Learning networks determined by the ratio of prior and data
Maomi Ueno

Online Semi-Supervised Learning on Quantized Graphs
Michal Valko, Branislav Kveton, Ling Huang, Daniel Ting

Speeding up the binary Gaussian process classification
Jarno Vanhatalo, Aki Vehtari

Efficient clustering with limited distance information
Konstantin Voevodski, Maria-Florina Balcan, Heiko Roeglin, Shang-Hua Teng, Yu Xia

Learning Why Things Change: The Difference-Based Causality Learner
Mark Voortman, Denver Dash, Marek Druzdzel

Primal View on Belief Propagation
Tomas Werner

Truthful Feedback for Sanctioning Reputation Mechanisms
Jens Witkowski

Rollout Sampling Policy Iteration for Decentralized POMDPs
Feng Wu, Shlomo Zilberstein, Xiaoping Chen

Semi-supervised Learning by Modeling Multiple-Annotator Expertise
Yan Yan, Romer Rosales, Glenn Fung, Jennifer Dy

Hybrid Generative/Discriminative Learning for Automatic Image Annotation
Shuang-Hong Yang, Jiang Bian, Hongyuan Zha

Solving Multistage Influence Diagrams using Branch-and-Bound Search
Changhe Yuan, Xiaojian Wu, Eric Hansen

A Convex Formulation for Learning Task Relationships in Multi-Task Learning
Yu Zhang, Dit-Yan Yeung

Multi-Domain Collaborative Filtering
Yu Zhang, Bin Cao, Dit-Yan Yeung

Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery
Kun Zhang, Bernhard Schölkopf, Dominik Janzing

Learning Structural Changes of Gaussian Graphical Models in Controlled Experiments
Bai Zhang, Yue Wang

Source separation and higher-order causal analysis of MEG and EEG
Kun Zhang, Aapo Hyvärinen

Automatic Tuning of Interactive Perception Applications
Qian Zhu, Branislav Kveton, Lily Mummert, Padmanabhan Pillai