Learning with Bounded Treewidth
Methods
Goal: structure learning of Bayesian networks with bounded treewidth.
Motivation: the complexity of exact inference in Bayesian networks is exponential to the treewidth of the graph.
Proposed methods
Publications
Articles in journals
Siqi Nie, Cassio P. de Campos, and Qiang Ji, "Efficient Learning of Bayesian Networks with Bounded Tree-width," International Journal of Approximate Reasoning (IJAR), 2016. [link]
Articles in conference proceedings
Siqi Nie, Cassio P. de Campos, and Qiang Ji, "Learning Bayesian Networks with Bounded Tree-width via Guided Search," in AAAI Conference on Artificial Intelligence (AAAI), 2016. Oral Presentation. [PDF] [supp]
Siqi Nie, Cassio P. de Campos, and Qiang Ji, "Learning Bounded Tree-width Bayesian Networks via Sampling," in Proceedings of the 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), 2015. [PDF]
Siqi Nie, Denis D. Mauá, Cassio P. de Campos, and Qiang Ji, "Advances in Learning Bayesian Networks of Bounded Treewidth," in Advances in Neural Information Processing Systems (NIPS), 2014. Spotlight Presentation. [PDF] [supp] [arXiv]