Karan Bhanot, Ioana Baldini, et al.
AIES 2023
Recent advances in Bayesian Network (BN) structure learning have focused on local-to-global learning, where the graph structure is learned via one local subgraph at a time. As a natural progression, we investigate parallel learning of BN structures via multiple learning agents simultaneously, where each agent learns one local subgraph at a time. We find that parallel learning can reduce the number of subgraphs requiring structure learning by storing previously queried results and communicating (even partial) results among agents. More specifically, by using novel rules on query subset and superset inference, many subgraph structures can be inferred without learning. We provide a sound and complete parallel structure learning (PSL) algorithm, and demonstrate its improved efficiency over state-of-the-art single-thread learning algorithms.
Karan Bhanot, Ioana Baldini, et al.
AIES 2023
Debarun Bhattacharjya, Dharmashankar Subramanian, et al.
IJCAI 2020
Debarun Bhattacharjya, Tian Gao, et al.
IJCAI 2020
Tian Gao, Qiang Ji
IJCAI 2016