NeurIPS 2019poster8 citations
Learning Bayesian Networks with Low Rank Conditional Probability Tables
Abstract
In this paper, we provide a method to learn the directed structure of a Bayesian network using data. The data is accessed by making conditional probability queries to a black-box model. We introduce a notion of simplicity of representation of conditional probability tables for the nodes in the Bayesian network, that we call `
BibTeX
@inproceedings{NEURIPS2019_187acf79,
author = {Barik, Adarsh and Honorio, Jean},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Learning Bayesian Networks with Low Rank Conditional Probability Tables},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/187acf7982f3c169b3075132380986e4-Paper.pdf},
volume = {32},
year = {2019}
}