Combining Prior Knowledge and Data: Beyond the Bayesian Framework

We explore this task in three contexts: classification (determining the subject of a newsgroup posting), control (learning to perform tasks such as driving a car up a mountain in simulation), and optimization (optimizing performance of linear algebra operations on different hardware platforms). For the text categorization problem, we introduce a novel algorithm which efficiently integrates prior knowledge into large margin classification. For reinforcement learning, we introduce a novel framework for defining and solving planning problems in terms of qualitative statements about the world. In compiler optimization, Bayesian prior based on an analytic model of hardware is combined with empirical measurements of performance of optimized code to determine the maximum-a-posteriori estimates of the optimization parameters.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Epshteyn, Arkady

Contributors dc:contributor

• Gerald DeJong

• Artificial Intelligence

Language dc:language

eng

Identifier

(MiAaPQ)AAI3269889

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81761