Uncertainty in Artificial Intelligence
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Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction
Stephen Bach, Bert Huang, Ben London, Lise Getoor
Abstract:
Graphical models for structured domains are powerful tools, but the computational complexities of combinatorial prediction spaces can force restrictions on models, or require approximate inference in order to be tractable. Instead of working in a combinatorial space, we use hinge-loss Markov random fields (HL-MRFs), an expressive class of graphical models with log-concave density functions over continuous variables, which can represent confidences in discrete predictions. This paper demonstrates that HL-MRFs are general tools for fast and accurate structured prediction. We introduce the first inference algorithm that is both scalable and applicable to the full class of HL-MRFs, and show how to train HL-MRFs with several learning algorithms. Our experiments show that HL-MRFs match or surpass the predictive performance of state-of-the-art methods, including discrete models, in four application domains.
Keywords:
Pages: 32-41
PS Link:
PDF Link: /papers/13/p32-bach.pdf
BibTex:
@INPROCEEDINGS{Bach13,
AUTHOR = "Stephen Bach and Bert Huang and Ben London and Lise Getoor",
TITLE = "Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction",
BOOKTITLE = "Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-13)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2013",
PAGES = "32--41"
}


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