Hinge-Loss Markov Random Fields and Probabilistic Soft Logic


This paper introduces hinge-loss Markov random fields (HL-MRFs), a new class of probabilistic graphical models particularly well-suited to large-scale structured prediction and learning. We derive HL-MRFs by unifying and then generalizing three different approaches to scalable inference in structured models: (1) randomized algorithms for MAX SAT, (2) local consistency relaxation for Markov random fields, and (3) reasoning about continuous information with fuzzy logic. To make HL-MRFs easy to construct and use, we next present probabilistic soft logic (PSL), a new probabilistic programming language for defining HL-MRFs for relational data. We then introduce a convex optimization algorithm based on message passing for exact MAP inference in HL-MRFs, as well as algorithms for weight learning.

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@article{Bach2015HingeLossMR, title={Hinge-Loss Markov Random Fields and Probabilistic Soft Logic}, author={Stephen H. Bach and Matthias Broecheler and Bert Huang and Lise Getoor}, journal={CoRR}, year={2015}, volume={abs/1505.04406} }