Date: Monday March 17, 2003
Room: 508 BBL
Schema-Based Synthesis of Data Analysis Programs
USRA/RIACS, NASA Ames Research Center
Automatic program synthesis is a formal approach to software
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
learning, or more precisely, to statistical data analysis, and I will
present the AutoBayes?
system currently under development at NASA Ames.
takes a specification in form of a statistical model, extracts
a graphical model (i.e., Bayesian network) from it, and then derives
by a process called schema-based synthesis. Schemas are generic
with their applicability conditions. Schemas come in different
some are derived from decomposition theorems for graphical models,
implement generic machine-learning algorithms like EM. Schemas are
recusively until irreducible subproblems occur which are then solved by
application of symbolic or numeric solvers. AutoBayes?
has been applied
a number of textbook and application problems, including clustering,
analysis, 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''. I will also discuss
the role of term rewriting techniques in the symbolic system and for
is joint work with W. Buntine, J. Schumann, and J. Whittle.