Convex Elicitation of Continuous Properties

Published in Neural Information Processing Systems, 2018

Recommended citation: Jessica Finocchiaro and Rafael Frongillo. (2018). "Convex Elicitation of Continuous Properties." Neural Information Processing Systems. https://papers.nips.cc/paper/8241-convex-elicitation-of-continuous-properties

Abstract: A property or statistic of a distribution is said to be elicitable if it can be expressed as the minimizer of some loss function in expectation. Recent work shows that continuous real-valued properties are elicitable if and only if they are identifiable, meaning the set of distributions with the same property value can be described by linear constraints. From a practical standpoint, one may ask for which such properties do there exist convex loss functions. In this paper, in a finite-outcome setting, we show that in fact every elicitable real-valued property can be elicited by a convex loss function. Our proof is constructive, and leads to convex loss functions for new properties.

Download paper here

Recommended citation: ‘Jessica Finocchiaro and Rafael Frongillo. (2018). "Convex Elicitation of Continuous Properties." Neural Information Processing Systems.’