Scalable Variational Inference in Log-supermodular Models

23 Feb 2015 Josip Djolonga Andreas Krause

We consider the problem of approximate Bayesian inference in log-supermodular models. These models encompass regular pairwise MRFs with binary variables, but allow to capture high-order interactions, which are intractable for existing approximate inference techniques such as belief propagation, mean field, and variants... (read more)

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