Visit of Prof. Jude Shavlik

01 August 2011

Statistical Relational Learning via Ensembles of First-Order Regression Trees

The two primary mathematical underpinnings of artificial intelligence have been first-order predicate logic and probability. Over the last decade or so there has been substantial research activity on approaches that combine the two, producing various forms of probabilistic logic. Within machine learning, this work is commonly called Statistical
Relational Learning (SRL).

At Wisconsin we have been investigating an approach to SRL where we learn probabilistic concepts expressed as a set of first-order regression trees. In such trees, nodes are expressions in first-order logic and the leaves are numbers (hence the phrase 'regression trees,' rather than the more common 'decision trees') . Our algorithm, called RDN-Boost, uses the numeric values at the leaves of these trees to produce its estimated probabilities. This talk will explain our RDN-Boost algorithm and present its performance on a variety of 'real world' testbeds, including comparison to alternate approaches.

Joint work with Sriraam Natarajan and Tushar Khot
of Wisconsin, Kristian Kersting of Frauhofer IAIS in Germany
and Bernd Gutmann of Katholieke Universiteit in Leuven, Belgium.

Short Bio
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Jude Shavlik is a Professor of Computer Sciences and of Biostatistics and Medical Informatics at the University of Wisconsin - Madison, and is a Fellow of the American Association for Artificial Intelligence. He has been at Wisconsin since 1988, following the receipt of his PhD from the University of Illinois for his work on Explanation-Based Learning. His current research interests include machine learning and computational biology, with an emphasis on using rich sources of training information, such as human-provided advice. He served for three years as editor-in-chief of the AI Magazine and serves on the editorial board of about a dozen journals. He chaired the 1998 International Conference on Machine Learning, co-chaired the First International Conference on Intelligent Systems for Molecular Biology in 1993, co-chaired the First International Conference on Knowledge Capture in 2001,
was conference chair of the 2003 IEEE Conference on Data Mining, and co-chaired the 2007 International Conference on Inductive Logic Programming. He was a founding member of both the board of the International Machine Learning Society and the board of the International Society for Computational Biology. He co-edited, with Tom Dietterich, "Readings in Machine Learning." His research has been supported by DARPA, NSF, NIH (NLM and NCI),
ONR, DOE, AT&T, IBM, and NYNEX.