We introduce a new local-to-global structure learning algorithm, called graph growing structure learning (GGSL), to learn Bayesian network (BN) structures. GGSL starts at a (random) node and then gradually expands the learned structure through a series of local learning steps. At each local learning step, the proposed algorithm only needs to revisit a subset of the learned nodes, consisting of the local neighborhood of a target, and therefore improves on both memory and time efficiency compared to traditional global structure learning approaches. GGSL also improves on the existing local-to-global learning approaches by removing the need for conflict-resolving AND-rules, and achieves better learning accuracy. We provide theoretical analysis for the local learning step, and show that GGSL outperforms existing algorithms on benchmark datasets. Overall, GGSL demonstrates a novel direction to scale up BN structure learning while limiting accuracy loss.
Summary/Notes »Author Information
Tian Gao (IBM Research)
Tian is currently a research staff member in IBM T. J. Watson Research Center. His research interests include machine learning, graphical models, causal discovery, reasoning, and applications.
Kshitij Fadnis (IBM)
Murray Campbell (IBM)
Related Events (a corresponding poster, oral, or spotlight)
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2017 Talk: Local-to-Global Bayesian Network Structure Learning »
Mon Aug 7th 04:06 -- 04:24 PM Room C4.9& C4.10
More from the Same Authors
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2019 Poster: DAG-GNN: DAG Structure Learning with Graph Neural Networks »
Yue Yu · Jie Chen · Tian Gao · Mo Yu -
2019 Oral: DAG-GNN: DAG Structure Learning with Graph Neural Networks »
Yue Yu · Jie Chen · Tian Gao · Mo Yu -
2019 Poster: Generalized Linear Rule Models »
Dennis Wei · Sanjeeb Dash · Tian Gao · Oktay Gunluk -
2019 Oral: Generalized Linear Rule Models »
Dennis Wei · Sanjeeb Dash · Tian Gao · Oktay Gunluk -
2018 Poster: Parallel Bayesian Network Structure Learning »
Tian Gao · Dennis Wei -
2018 Oral: Parallel Bayesian Network Structure Learning »
Tian Gao · Dennis Wei
