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

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

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.

Author Information

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

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