Open Problem: Property Elicitation and Elicitation Complexity

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Rafael Frongillo, Ian Kash, Stephen Becker ;
29th Annual Conference on Learning Theory, PMLR 49:1655-1658, 2016.

Abstract

The study of property elicitation is gaining ground in statistics and machine learning as a way to view and reason about the expressive power of emiprical risk minimization (ERM). Yet beyond a widening frontier of special cases, the two most fundamental questions in this area remain open: which statistics are elicitable (computable via ERM), and which loss functions elicit them? Moreover, recent work suggests a complementary line of questioning: given a statistic, how many ERM parameters are needed to compute it? We give concrete instantiations of these important questions, which have numerous applications to machine learning and related fields.

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