Poster Sessions

Linear Algebra Approach to Separable Bayesian Networks
Chalee Asavathiratham Gene Expression Time Course Clustering with Countably Infinite Hidden Markov Models
Matthew Beal and Praveen Krishnamurthy Cutset Sampling with Likelihood Weighting
Bozhena Bidyuk and Rina Dechter Graphical Condition for Identification in Recursive SEM
Carlos Brito and Judea Pearl Sensitivity Analysis for Threshold Decision Making with Dynamic Networks
Theodore Charitos and Linda C. van der Gaag Direct and Indirect Effects of Sequential Treatments
Vanessa Didelez, A. Philip Dawid and Sara Geneletti Fitting Graphical Interaction Models to Multivariate Time Series
Michael Eichler Residual Belief Propagation: Informed Scheduling for Asynchronous Message Passing
Gal Elidan, Ian McGraw and Daphne Koller Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian Networks
Nir Friedman and Raz Kupferman A New Axiomatization for Likelihood Gambles
Phan Giang Inequality Constraints in Causal Models with Hidden Variables
Changsung Kang and Jin Tian Stratified Analysis of `Probabilities of Causation'
Manabu Kuroki and Zhihong Cai Sequential Document Representations and Simplicial Curves
Guy Lebanon Reasoning about Uncertainty in Metric Spaces
Seunghwan Lee Visualization of Collaborative Data
Guobiao Mei and Christian Shelton Approximate Separability for Weak Interaction in Dynamic Systems
Avi Pfeffer Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation
Ian Porteous, Alex Ihler, Padhriac Smyth and Max Welling A Simple Approach for Finding the Globally Optimal Bayesian Network Structure
Tomi Silander and Petri Myllymäki Variable Noise and Dimensionality Reduction for Sparse Gaussian Processes
Edward Snelson and Zoubin Ghahramani A Non-Parametric Bayesian Method for Inferring Hidden Causes
Frank Wood, Thomas L. Griffiths and Zoubin Ghahramani Bayesian Multicategory Support Vector Machines
Zhihua Zhang and Michael I. Jordan

Robust Learning Equilibrium
Itai Ashlagi, Dov Monderer and Moshe Tennenholtz An Efficient Triplet-based Algorithm for Evidential Reasoning
Yaxin Bi and Jiwen Guan On the Robustness of Most Probable Explanations
Hei Chan and Adnan Darwiche Discriminative Learning via Semidefinite Probabilistic Models
Koby Crammer and Amir Globerson A Concentration Theorem for Projections
Sanjoy Dasgupta, Daniel Hsu and Nakul Verma Chi-square Tests Driven Method for Learning the Structure of Factored MDPs
Thomas Degris, Olivier Sigaud and Pierre-Henri Wuillemin Matrix Tile Analysis
Inmar Givoni, Vincent Cheung and Brendan Frey Convex Structure Learning for Bayesian Networks: Polynomial Feature Selection and Approximate Ordering
Yuhong Guo and Dale Schuurmans The AI&M Procedure for Learning from Incomplete Data
Manfred Jaeger Predicting Conditional Quantiles via Reduction to Classification
John Langford, Roberto Oliveira and Bianca Zadrozny Faster Gaussian Summation: Theory and Experiment
Dongryeol Lee and Alexander Gray Belief Update in CLG Bayesian Networks With Lazy Propagation
Anders Madsen A Theoretical Study of Y Structures for Causal Discovery
Subramani Mani, Gregory Cooper and Peter Spirtes A Compact, Hierarchical Q-Function Decomposition
Bhaskara Marthi, Stuart Russell and David Andre MCMC for Doubly-Intractable Distributions
Iain Murray, Zoubin Ghahramani and David MacKay Identifying the Relevant Nodes Without Learning the Model
Jose M. Peña, Roland Nilsson, Johan Björkegren and Jesper Tegnér From Influence Diagrams to Multi-Operator Cluster DAGs
Cedric Pralet, Thomas Schiex and Gerard Verfaillie Ranking by Dependence--A Fair Criteria
Harald Steck Axiomatic Foundations for a Class of Generalized Expected Utility: Algebraic Expected Utility
Paul Weng Stochastic Optimal Control in Continuous Space-Time Multi-Agent Systems
Wim Wiegerinck, Bart van den Broek and Bert Kappen Infinite Hidden Relational Models
Zhao Xu, Volker Tresp, Kai Yu and Hans-Peter Kriegel