Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction
This addresses the problem of tractable inference in structured prediction for machine learning practitioners, offering a novel approach that is not incremental.
The paper tackles the computational complexity of structured prediction in graphical models by introducing hinge-loss Markov random fields (HL-MRFs), which use continuous variables for fast and accurate inference, and demonstrates that HL-MRFs match or surpass state-of-the-art methods in four application domains.
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.