Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction

Stephen Bach, Bert Huang, Ben London, Lise Getoor
Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:272-281, 2013.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR11-bach13a, title = {Hinge-loss {M}arkov Random Fields: Convex Inference for Structured Prediction}, author = {Bach, Stephen and Huang, Bert and London, Ben and Getoor, Lise}, booktitle = {Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence}, pages = {272--281}, year = {2013}, editor = {Nicholson, Ann and Smyth, Padhraic}, volume = {R11}, series = {Proceedings of Machine Learning Research}, month = {12--14 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r11/main/assets/bach13a/bach13a.pdf}, url = {https://proceedings.mlr.press/r11/bach13a.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction %A Stephen Bach %A Bert Huang %A Ben London %A Lise Getoor %B Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2013 %E Ann Nicholson %E Padhraic Smyth %F pmlr-vR11-bach13a %I PMLR %P 272--281 %U https://proceedings.mlr.press/r11/bach13a.html %V R11 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Bach, S., Huang, B., London, B. & Getoor, L.. (2013). Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction. Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R11:272-281 Available from https://proceedings.mlr.press/r11/bach13a.html. Reissued by PMLR on 04 October 2026.

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