[talks] 1-2pm Wed May 7 talk on learning structured bayesian networks in room 302

Jennifer Rexford jrex at CS.Princeton.EDU
Wed Apr 23 19:58:21 EDT 2008

Title: *Learning Structured Bayesian Networks: Combining Abstraction 
Hierarchies and Tree-Structured Conditional Probability Tables
Speaker: **Marie desJardins*, University of Maryland
Date/time: 1-2pm Wed May 7
Location: room 302 in the CS building


    In this talk, I will describe our research on incorporating
    background knowledge in the form of feature hierarchies during
    Bayesian network learning. Feature hierarchies enable the learning
    system to aggregate categorical variables in meaningful ways, thus
    enabling an appropriate "discretization" for a categorical variable.
    In addition, by choosing the appropriate level of abstraction for
    the parent of a node, we also support compact representations for
    the local probability models in the network. We combine this notion
    of selecting an appropriate abstraction with context-specific
    independence representations, which capture local ndependence
    relationships among the random variables in the Bayesian network.
    Capturing this local structure is important because it reduces the
    number of parameters required to represent the distribution. This
    can lead to more robust parameter estimation and structure
    selection, more efficient inference algorithms, and more
    interpretable models.

    I will describe our primary contribution, the Tree-Abstraction-Based
    Search (TABS) algorithm, which learns a data distribution by
    inducing the graph structure and parameters of a Bayesian network
    from training data. TABS combines tree structure and attribute-value
    hierarchies to compactly represent conditional probability tables.
    In order to construct the attribute-value hierarchies, we
    investigate two data-driven techniques: a global clustering method,
    which uses all of the training data to build the attribute-value
    hierarchies, and can be performed as a preprocessing step; and a
    local clustering method, which uses only the local network structure
    to learn attribute-value hierarchies. Empirical results in several
    benchmark domains show that (1) combining tree structure and
    attribute-value hierarchies improves the accuracy of generalization,
    while providing a significant reduction in the number of parameters
    in the learned networks, and (2) data-derived hierarchies perform as
    well or better than expert-provided hierarchies.

    BIOGRAPHY Dr. Marie desJardins is an associate professor in the
    Department of Computer Science and Electrical Engineering at the
    University of Maryland, Baltimore County. Her research is in
    artificial intelligence, focusing on the areas of machine learning,
    multi-agent systems, planning, interactive AI techniques,
    information management, reasoning with uncertainty, and decision

    Dr. desJardins can be contacted at the Dept. of Computer Science and
    Electrical Engineering, University of Maryland Baltimore County,
    1000 Hilltop Circle, Baltimore MD 21250, mariedj at cs.umbc.edu,(410)

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