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Broad Area Colloquium for Artificial Intelligence,
Geometry, Graphics, Robotics and Vision

Learning Probabilistic Models from Relational Data

Daphne Koller
Stanford University

Monday, November 26, 2001, 4:15PM
Gates B01


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 the domain. However, many real-world domains are best described by relational 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 authors. 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 techniques to pattern discovery in complex real-world data sets, including web data, scientific papers, and biomedical data.

Joint work with Lise Getoor, Nir Friedman, Avi Pfeffer, Eran Segal, and Ben Taskar.

About the Speaker

Daphne Koller received her PhD from Stanford University in 1994. After a 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. She 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 Engineers (PECASE) in 1999, and the IJCAI Computers and Thought Award at the IJCAI 2001 conference.


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