Abstract

Cutset networks — OR (decision) trees that have Bayesian networks whose treewidth is bounded by one at each leaf — are a new class of tractable probabilistic models that admit fast, polynomial-time inference and learning algorithms. This is unlike other state-of-the-art tractable models such as thin junction trees, arithmetic circuits and sum-product networks in which inference is fast and efficient but learning can be notoriously slow. In this paper, we take advantage of this unique prop-erty to develop fast algorithms for learning ensembles of cutset networks. Specifically, we consider general-ized additive mixtures of cutset networks and develop sequential boosting-based and parallel bagging-based approaches for learning them from data. We demon-strate, via a thorough experimental evaluation, that our new algorithms are superior to competing approaches in terms of test-set log-likelihood score and learning time.