Hinge-Loss Markov Random Fields and Probabilistic Soft Logic

Abstract

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.

Extracted Key Phrases

02040201520162017
Citations per Year

70 Citations

Semantic Scholar estimates that this publication has 70 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@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} }