Mauro Scanagatta — accepted AI-conference papers

2016

Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables

Mauro Scanagatta, Giorgio Corani, Cassio P de Campos, Marco Zaffalon

NeurIPS 2016poster

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 th…

Cited by 56 Source ↗PDF ↗Scholar ↗

2015

Learning Bayesian Networks with Thousands of Variables

Mauro Scanagatta, Cassio P de Campos, Giorgio Corani, Marco Zaffalon

NeurIPS 2015poster

We present a method for learning Bayesian networks from data sets containingthousands of variables without the need for structure constraints. Our approachis made of two parts. The first is a novel algorithm that effectively explores thespace of possible parent sets of a node. It guides the explorat…

Cited by 159 Source ↗PDF ↗Scholar ↗