Uncertainty in Artificial Intelligence
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New Advances and Theoretical Insights into EDML
Khaled Refaat, Arthur Choi, Adnan Darwiche
EDML is a recently proposed algorithm for learning MAP parameters in Bayesian networks. In this paper, we present a number of new advances and insights on the EDML algorithm. First, we provide the multivalued extension of EDML, originally proposed for Bayesian networks over binary variables. Next, we identify a simplified characterization of EDML that further implies a simple fixed-point algorithm for the convex optimization problem that underlies it. This characterization further reveals a connection between EDML and EM: a fixed point of EDML is a fixed point of EM, and vice versa. We thus identify also a new characterization of EM fixed points, but in the semantics of EDML. Finally, we propose a hybrid EDML/EM algorithm that takes advantage of the improved empirical convergence behavior of EDML, while maintaining the monotonic improvement property of EM.
Pages: 705-714
PS Link:
PDF Link: /papers/12/p705-refaat.pdf
AUTHOR = "Khaled Refaat and Arthur Choi and Adnan Darwiche",
TITLE = "New Advances and Theoretical Insights into EDML",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "705--714"

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