Broad Area Colloquium for Artificial Intelligence,
Geometry, Graphics, Robotics and Vision
Learning Probabilistic Models from Relational Data
Monday, November 26, 2001, 4:15PM
Bayesian networks are a compact and natural representation for complex
probabilistic models. They use graphical notation to encode domain
structure: the direct probabilistic dependencies between variables in
domain. However, many real-world domains are best described by
in which instances of multiple types are related to each other
in complex ways. For example, in a scientific paper domain, papers are
related to each other via citation, and are also related to their
Bayesian networks are attribute-based, making it difficult to represent
the rich relational structure of complex domains involving multiple
entities that interact with each other. The talk will describe
probabilistic relational models (PRMs), a new probabilistic modeling
language suitable for relational domains. PRMs extend the language of
Bayesian networks with the expressive power of object-relational
languages. They model the uncertainty over the attributes of objects
in the domain as well as uncertainty over the relations between objects.
The talk will present techniques for automatically inducing PRMs
directly from a relational data set, and applications of these
to pattern discovery in complex real-world data sets, including web
scientific papers, and biomedical data.
Joint work with Lise Getoor, Nir Friedman, Avi Pfeffer, Eran Segal, and
About the Speaker
Daphne Koller received her PhD from Stanford University in 1994. After
two-year postdoc at Berkeley, she returned to Stanford, where she is now
an Associate Professor in the Computer Science Department. Her main
research interest is in creating large-scale systems that reason and act
under uncertainty, using techniques from decision theory and economics.
Daphne Koller is the author of over 70 refereed publications, which have
appeared in AI, theoretical computer science, and economics venues.
She is the co-chair of the recent UAI 2001 conference, has served on
numerous program committees, and as associate editor of the Journal of
Artificial Intelligence Research and of the Machine Learning Journal.
was awarded the Arthur Samuel Thesis Award in 1994, the Sloan
Foundation Faculty Fellowship in 1996, the ONR Young Investigator Award
in 1998, the Presidential Early Career Award for Scientists and
(PECASE) in 1999, and the IJCAI Computers and Thought Award at the
IJCAI 2001 conference.
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