Abstract
Automatic program synthesis is a formal approach to software development, in which efficiently executable programs are automatically derived from high-level specifications. It has successfully been applied to a number of domains, for example, celestial mechanics, transportation scheduling, or option pricing. In this talk I will discuss its application to machine learning, or more precisely, to statistical data analysis, and I will present the AutoBayes system currently under development at NASA Ames.

AutoBayes takes a specification in form of a statistical model, extracts a graphical model (i.e., Bayesian network) from it, and then derives code by a process called schema-based synthesis. Schemas are generic algorithms with their applicability conditions. Schemas come in different ''flavors''; some are derived from decomposition theorems for graphical models, others implement generic machine-learning algorithms like EM. Schemas are applied recusively until irreducible subproblems occur which are then solved by the application of symbolic or numeric solvers. AutoBayes has been applied to a number of textbook and application problems, including clustering (using EM), changepoint detection, and software reliability estimation.

In the talk, I will discuss some examples and their derivation processes in more detail and demonstrate the system ''live''.

AutoBayes is joint work with W. Buntine, J. Schumann, and J. Whittle.

e-mail: fisch@email.arc.nasa.gov
web: http://ase.arc.nasa.gov/people/fischer/

ASTEC seminar
June 5, 2002

Place: Information technology, Uppsala University
Room: 1113
Time: 13.15-14.00 (+ discussions)

Room 1113 is in building 1, floor 1, room 13 (in the southern part of the building).

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There will be an extended period for discussions after the seminar.

Speakers are encouraged to give an short (5 min) introduction to the subject at the begining of the talk.
Listeners are excused if they have to leave after 16.00.