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Majorization for CRFs and Latent Likelihoods
Tony Jebara · Anna Choromanska

Wed Dec 05 10:10 AM -- 10:14 AM (PST) @ Harveys Convention Center Floor, CC
in Spotlight Session 5 »

The partition function plays a key role in probabilistic modeling including conditional random fields, graphical models, and maximum likelihood estimation. To optimize partition functions, this article introduces a quadratic variational upper bound. This inequality facilitates majorization methods: optimization of complicated functions through the iterative solution of simpler sub-problems. Such bounds remain efficient to compute even when the partition function involves a graphical model (with small tree-width) or in latent likelihood settings. For large-scale problems, low-rank versions of the bound are provided and outperform LBFGS as well as first-order methods. Several learning applications are shown and reduce to fast and convergent update rules. Experimental results show advantages over state-of-the-art optimization methods.

Author Information

Tony Jebara (Spotify)
Anna Choromanska (Columbia University)

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