Learning Programs: A Hierarchical Bayesian Approach

We are interested in learning programs for multiple related tasks given only a few training examples per task. Since the program for a single task is underdetermined by its data, we introduce a nonparametric hierarchical Bayesian prior over programs which shares statistical strength across multiple tasks. The key challenge is to parametrize this multi-task sharing. For this, we introduce a new representation of programs based on combinatory logic and provide an MCMC algorithm that can perform safe program transformations on this representation to reveal shared inter-program substructures.

• Type: Conference
• Year: 2010
• Where: ICML
• Authors: Percy Liang, Michael I. Jordan, Dan Klein
• Keywords: ICML 2010, Machine Learning, Multiple Related Tasks, Multiple Tasks, Shares Statistical Strength