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.
|Title of host publication||Advances in Neural Information Processing Systems 29 (NIPS 2016)|
|Publisher||NIPS Foundation, Inc|
|Publication status||Accepted - 12 Aug 2016|
|Event||Annual Conference on Neural Information Processing Systems - Centre Convencions Internacional Barcelona, Barcelona, Spain|
Duration: 05 Dec 2016 → 10 Dec 2016
|Conference||Annual Conference on Neural Information Processing Systems|
|Abbreviated title||NIPS 2016|
|Period||05/12/2016 → 10/12/2016|
Bibliographical noteDouble blind peer reviewed by multiple reviewers. Acc. rate 22%.
Scanagatta, M., Corani, G., de Campos, C. P., & Zaffalon, M. (Accepted/In press). Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables. In Advances in Neural Information Processing Systems 29 (NIPS 2016) NIPS Foundation, Inc.