From MAP to Marginals: Variational Inference in Bayesian Submodular Models

  title={From MAP to Marginals: Variational Inference in Bayesian Submodular Models},
  author={Josip Djolonga and Andreas Krause},
Submodular optimization has found many applications in machine learning and beyond. We carry out the first systematic investigation of inference in probabilistic models defined through submodular functions, generalizing regular pairwise MRFs and Determinantal Point Processes. In particular, we present L-FIELD, a variational approach to general log-submodular and log-supermodular distributions based on suband supergradients. We obtain both lower and upper bounds on the log-partition function… CONTINUE READING
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