Variational Inference in Mixed Probabilistic Submodular Models

Variational Inference in Mixed Probabilistic Submodular Models

Abstract

We consider the problem of variational inference in probabilistic models with bothlog-submodular and log-supermodular higher-order potentials. These models canrepresentarbitrary distributionsover binary variables, and thus generalize thecommonly used pairwise Markov random fields and models with log-supermodularpotentials only, for which efficient approximate inference algorithms are known.While inference in the considered models is #P-hard in general, we present effi-cient approximate algorithms exploiting recent advances in the field of discreteoptimization. We demonstrate the effectiveness of our approach in a large set ofexperiments, where our model allows reasoning about preferences over sets ofitems with complements and substitutes.

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Authors
  • Djolonga, Josip
  • Tschiatschek, Sebastian
  • Krause, Andreas
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Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
Neural Information Processing Systems (NIPS)
Divisions
Data Mining and Machine Learning
Event Location
Barcelona, Spain
Event Type
Conference
Event Dates
05.-10.12.2016
Series Name
Advances in Neural Information Processing Systems 29 (NIPS 2016)
Date
5 December 2016
Official URL
https://papers.nips.cc/paper/6225-variational-infe...
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