## 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**

**Mixed Integer Linear Programming:**an exact method that combines the integer programming formulation of BN structure learning with a novel approach to empose the treewidth constraint. The exact algorithm can exactly learn the BN structure with 30 nodes.

**Heuristic search:**an approximate algorithm based on the family of k-trees. Propose an efficient algorithm for search for k-trees, and then learn the structure of the BN as a subgraph of the k-tree. Propose the hill climbing and A* search algorithms. The approximate algorithm can scale up to network with 100 nodes.

### 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]