Abstract:
Bayesian networks are a widely used graphical model in machine learning with applications in diverse and numerous fields. Despite the wealth of literature on structure learning and its applications, parallel algorithms for structure learning have only begun to appear recently. In this talk, I will present recent research from my group on developing parallel exact and heuristic algorithms for Bayesian network structure learning. Exact learning is an NP-hard problem, limiting its use to small problem sizes. I will first present a work and space optimal parallel algorithm for exact structure learning, and its extensions to networks with bounded in-degree. I will then present a parallel heuristic structure learning algorithm that can scale to large networks. As a demonstration, we reconstructed genome-scale networks of the model plant Arabidopsis thaliana from 11,700 microarray experiments using 1.57 million cores of the Tianhe-2 Supercomputer.
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