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An Incremental Subgradient Algorithm for Approximate MAP Estimation in Graphical Models

An Incremental Subgradient Algorithm for Approximate MAP Estimation in Graphical Models

This video was recorded at NIPS Workshops, Whistler 2010. We present an incremental subgradient algorithm for approximate computation of maximum-a-posteriori (MAP) states in cyclic graphical models. Its most striking property is its immense simplicity: each iteration requires only the solution of a sequence of trivial optimization problems. The algorithm can be equally understood as a degenerated dual decomposition scheme or as minimization of a degenerated tree-reweighted upper bound and assumes a form that is reminiscent of message-passing. Despite (or due to) its conceptual simplicity, it is equipped with important theoretical guarantees and exposes strong empirical performance.

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