UAI 2005

21st Conference on Uncertainty in Artificial Intelligence
Poster Sessions

July 26th-July 29th 2005

University of Edinburgh
Edinburgh, Scotland

Wednesday July 27th. Poster Highlights 3:00, Session 3:30

  • On the Value of Correlation
    Itai Ashlagi, Dov Monderer and Moshe Tennenholtz

  • Ordering-based Search: A Simple and Effective Algorithm for Learning Bayesian Networks
    Marc Teyssier and Daphne Koller

  • A Transformational Characterization of Markov Equivalence for Directed Acyclic Graphs with Latent Variables
    Jiji Zhang and Peter Spirtes

  • On Privacy-preserving Histograms
    Shuchi Chawla, Cynthia Dwork, Frank McSherry and Kunal Talwar

  • Metrics for Markov Decision Processes with Infinite State Spaces
    Norman F. Ferns, Prakash Panangaden and Doina Precup

  • On the Number of Experiments sufficient and in the worst wase necessary to identify all Causal Relations among N Variables
    Frederick Eberhardt, Clark Glymour and Richard Scheines

  • Cliquewise Training for Undirected Models
    Charles Sutton and Andrew McCallum

  • Modifying Bayesian Networks by Probability Constraints
    Yun Peng and Zhongli Ding

  • Two-way Latent Grouping Model for User Preference Prediction
    Eerika Savia, Kai Puolamäki, Janne Sinkkonen and Samuel Kaski

  • Learning Bayesian Network Parameters with Prior Knowledge about Context-Specific Qualitative Influences.
    Ad Feelders and Linda C. van der Gaag

  • Graphical Identifications for Total Effects by using Surrogate Variables
    Manabu Kuroki, Zhihong Cai and Hiroki Motogaito

  • MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPS
    Daniel Szer, Francois Charpillet, Shlomo Zilberstein

  • Asynchronous Dynamic Bayesian Networks
    Avi Pfeffer and Terry Tai

  • Maximum Margin Bayesian Networks
    Yuhong Guo, Dana Wilkinson and Dale Schuurmans

  • Learning to Classify Individuals Based on Group Statistics
    Hendrik Kuck and Nando de Freitas

  • Planning in POMDPS using Multiplicity Automata
    Eyal Even-Dar and Sham M. Kakade and Yishay Mansour

  • Stable Independence in Perfect Maps
    Peter de Waal and Linda C. van der Gaag

  • Description Logics with Fuzzy Concrete Domains
    Umberto Straccia

  • On Bayesian Network Approximation by Edge Deletion
    Arthur Choi, Hei Chan  and Adnan Darwiche

  • Importance Sampling in Bayesian Networks: An Influence-based Approximation Strategy for Importance Functions
    Changhe Yuan and Marek J. Druzdzel

  • Qualitative Decision Making under Possibilistic Uncertainty: toward more Discriminant Criteria
    Paul Weng

  • A Model for Reasoning with Uncertain Rules in Event Composition Systems
    Segev Wasserkrug, Avigdor Gal and Opher Etzion

  • Bounding the Uncertainty of Graphical Games: The Complexity of Simple Requirements, Pareto and Strong Nash Equilibria
    Gianluigi Greco and Francesco Scarcello

  • Unsupervised Activity Discovery and Characterization from Event-Streams
    Raffay Hamid, Siddhartha Maddi, Amos Johnson, Aaron Bobick, Irfan Essa, and Charles Isbell

  • Cost Sensitive Reachability Heuristics for Handling State Uncertainty
    Daniel Bryce and Subbarao Kambhampati

  • A Function Approximation Approach to Estimation of Policy Gradient for POMDP with Structured Policies
    Huizhen Yu

  • A Differential Semantics of Lazy AR Propagation
    Anders L Madsen

  • Modeling Transportation Routines using Hybrid Dynamic Mixed Networks
    Vibhav Gogate, Rina Dechter, Bozhena Bidyuk, Craig Rindt and James Marca

  • Local Markov Property for Models Satisfying Composition Axiom
    Changsung Kang and Jin Tian

Thursday July 28th. Poster Highlights 3:00, Session 3:30

  • The Relationship between AND/OR Search Spaces And Variable Elimination
    Robert Mateescu and Rina Dechter

  • A Heuristic Search Algorithm for Solving First-Order MDPS
    Eldar Karabaev and Olga Skvortsova

  • A Unified Setting for Inference and Decision: an Argumentation-based approach
    Leila Amgoud

  • Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graph Models with Latent Variables
    R. Ayesha Ali, Thomas S. Richardson, Peter Spirtes and Jiji Zhang

  • Nonparametric Bayesian Logic
    Peter Carbonetto, Jacek Kisynski, Nando de Freitas and David Poole

  • Exploiting Evidence-dependent Sensitivity Bounds
    Silja Renooij, Linda C. van der Gaag

  • Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework
    Markus Harva, Tapani Raiko, Antti Honkela, Harri Valpola, and Juha Karhunen

  • Approximate Linear Programming for First-Order MDPS
    Scott Sanner and Craig Boutilier

  • Counterexample-guided Planning
    Krishnendu Chatterjee, Thomas A. Henzinger, Ranjit Jhala, and Rupak Majumdar

  • Efficient Algorithm for Estimation of Qualitative Expected Utility in Possibilistic Case-Based Reasoning
    Jakub Brzostowski and Ryszard Kowalczyk

  • Existence and Finiteness Conditions for Risk-Sensitive Planning: Results and Conjectures
    Yaxin Liu and Sven Koenig

  • Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement
    Jin Tian

  • A Revision-based Approach to Resolving Conflicting Information
    Guilin Qi, Weiru Liu and David A. Bell

  • Exploiting Evidence in Probabilistic Inference
    Mark Chavira, David Allen and Adnan Darwiche

  • Models for Truthful Online Double Auctions
    Jonathan Bredin and David Parkes

  • Hybrid Bayesian Networks with Linear Deterministic Variables
    Barry R. Cobb and Prakash P. Shenoy

  • Learning from Sparse Data by Exploiting Monotonicity Constraints
    Eric E. Altendorf, Angelo C. Restificar and Thomas G. Dietterich

  • Obtaining Calibrated Probabilities from Boosting
    Alexandru Niculescu-Mizil and Rich Caruana

  • Of Starships and Klingons: Bayesian Logic for the 23rd Century
    Kathryn B. Laskey and Paulo C. G. da Costa

  • On the Detection of Concept changes in Time-varying data stream by Testing Exchangeability
    Shen-Shyang Ho and Harry Wechsler

  • Learning Factor Graphs in Polynomial Time & Sample Complexity
    Pieter Abbeel, Daphne Koller and Andrew Y. Ng

  • Expectation Propagation for Continuous Time Bayesian Networks
    Uri Nodelman, Daphne Koller and Christian R. Shelton

  • Predictive Linear-Gaussian Models of Stochastic Dynamical Systems
    Matthew Rudary, Satinder Singh, and David Wingate

  • Self-Confirming Price Prediction for Bidding in Simultaneous Ascending Auctions
    Anna Osepayshvili, Michael P. Wellman, Daniel M. Reeves, and Jeffrey K. MacKie-Mason

  • Unsupervised Spectral Learning
    Susan Shortreed and Marina Meila

  • Use of Dempster-Shafer Conflict Metric to Detect Interpretation Inconsistency
    Jennifer Carlson and Robin S. Murphy

  • Mining Associated Text and Images with Dual-Wing Harmoniums
    Eric P. Xing, Rong Yan and Alexander G. Hauptmann

  • A submodular-supermodular Procedure with applications to Discriminative Structure Learning
    Mukund Narasimhan and Jeff Bilmes

  • Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints
    Vibhav Gogate and Rina Dechter

  • Counterfactual Reasoning in Linear Structural Equation Models
    Zhihong Cai and Manabu Kuroki