Learning Programs: A Hierarchical Bayesian Approach
Abstract
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.
Cite
Text
Liang et al. "Learning Programs: A Hierarchical Bayesian Approach." International Conference on Machine Learning, 2010.Markdown
[Liang et al. "Learning Programs: A Hierarchical Bayesian Approach." International Conference on Machine Learning, 2010.](https://mlanthology.org/icml/2010/liang2010icml-learning/)BibTeX
@inproceedings{liang2010icml-learning,
title = {{Learning Programs: A Hierarchical Bayesian Approach}},
author = {Liang, Percy and Jordan, Michael I. and Klein, Dan},
booktitle = {International Conference on Machine Learning},
year = {2010},
pages = {639-646},
url = {https://mlanthology.org/icml/2010/liang2010icml-learning/}
}