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Poster
Decomposition Bounds for Marginal MAP
Wei Ping · Qiang Liu · Alexander Ihler

Mon Dec 07 04:00 PM -- 08:59 PM (PST) @ 210 C #68
in Poster Session »

Marginal MAP inference involves making MAP predictions in systems defined with latent variables or missing information. It is significantly more difficult than pure marginalization and MAP tasks, for which a large class of efficient and convergent variational algorithms, such as dual decomposition, exist. In this work, we generalize dual decomposition to a generic powered-sum inference task, which includes marginal MAP, along with pure marginalization and MAP, as special cases. Our method is based on a block coordinate descent algorithm on a new convex decomposition bound, that is guaranteed to converge monotonically, and can be parallelized efficiently. We demonstrate our approach on various inference queries over real-world problems from the UAI approximate inference challenge, showing that our framework is faster and more reliable than previous methods.

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Author Information

Wei Ping (UC Irvine)
Qiang Liu (MIT)
Alexander Ihler (UC Irvine)

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