Uncertainty in Artificial Intelligence
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Variational Algorithms for Marginal MAP
Qiang Liu, Alexander Ihler
Abstract:
Marginal MAP problems are notoriously difficult tasks for graphical models. We derive a general variational framework for solving marginal MAP problems, in which we apply analogues of the Bethe, tree-reweighted, and mean field approximations. We then derive a "mixed" message passing algorithm and a convergent alternative using CCCP to solve the BP-type approximations. Theoretically, we give conditions under which the decoded solution is a global or local optimum, and obtain novel upper bounds on solutions. Experimentally we demonstrate that our algorithms outperform related approaches. We also show that EM and variational EM comprise a special case of our framework.
Keywords:
Pages: 453-462
PS Link:
PDF Link: /papers/11/p453-liu.pdf
BibTex:
@INPROCEEDINGS{Liu11,
AUTHOR = "Qiang Liu and Alexander Ihler",
TITLE = "Variational Algorithms for Marginal MAP",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "453--462"
}


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