NeurIPS 2016poster56 citations
Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables
Mauro Scanagatta, Giorgio Corani, Cassio P de Campos, Marco Zaffalon
Abstract
We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. Bounding the treewidth of a Bayesian network greatly reduces the complexity of inferences. Yet, being a global property of the graph, it considerably increases the difficulty of the learning process. Our novel algorithm accomplishes this task, scaling both to large domains and to large treewidths. Our novel approach consistently outperforms the state of the art on experiments with up to thousands of variables.
BibTeX
@inproceedings{NIPS2016_e2a2dcc3,
author = {Scanagatta, Mauro and Corani, Giorgio and de Campos, Cassio P and Zaffalon, Marco},
booktitle = {Advances in Neural Information Processing Systems},
editor = {D. Lee and M. Sugiyama and U. Luxburg and I. Guyon and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables},
url = {https://proceedings.neurips.cc/paper_files/paper/2016/file/e2a2dcc36a08a345332c751b2f2e476c-Paper.pdf},
volume = {29},
year = {2016}
}