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Elicitation is the study of statistics or properties which are computable via empirical risk minimization. While several recent papers have approached the general question of which properties are elicitable, we suggest that this is the wrong question---all properties are elicitable by first eliciting the entire distribution or data set, and thus the important question is how elicitable. Specifically, what is the minimum number of regression parameters needed to compute the property?Building on previous work, we introduce a new notion of elicitation complexity and lay the foundations for a calculus of elicitation. We establish several general results and techniques for proving upper and lower bounds on elicitation complexity. These results provide tight bounds for eliciting the Bayes risk of any loss, a large class of properties which includes spectral risk measures and several new properties of interest.
Author Information
Rafael Frongillo (CU Boulder)
Ian Kash (Microsoft)
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2016 Workshop: Crowdsourcing and Machine Learning »
Adish Singla · Rafael Frongillo · Matteo Venanzi -
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2015 Poster: A Market Framework for Eliciting Private Data »
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2015 Poster: Convergence Analysis of Prediction Markets via Randomized Subspace Descent »
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2014 Workshop: NIPS’14 Workshop on Crowdsourcing and Machine Learning »
David Parkes · Denny Zhou · Chien-Ju Ho · Nihar Bhadresh Shah · Adish Singla · Jared Heyman · Edwin Simpson · Andreas Krause · Rafael Frongillo · Jennifer Wortman Vaughan · Panagiotis Papadimitriou · Damien Peters -
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2013 Spotlight: How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal »
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2011 Poster: A Collaborative Mechanism for Crowdsourcing Prediction Problems »
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2011 Oral: A Collaborative Mechanism for Crowdsourcing Prediction Problems »
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