Distributionally Robust Graphical Models

Rizal Fathony

Ashkan Rezaei

Mohammad Ali Bashiri

Xinhua Zhang (张歆华)

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Brian D. Ziebart

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ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), pp. 8344-8355, 2018.

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Keywords:

loss functionslearning processtime complexitystructured predictiongraphical modelsMore(3+)

Abstract:

In many structured prediction problems, complex relationships between variables are compactly defined using graphical structures. The most prevalent graphical prediction methods-probabilistic graphical models and large margin methods-have their own distinct strengths but also possess significant drawbacks. Conditional random fields (CRFs)...More

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