no code implementations • 29 Jun 2022 • Jessie Finocchiaro, Rafael M. Frongillo, Bo Waggoner
Using these results, we establish that indirect elicitation, a necessary condition for consistency, is also sufficient when working with polyhedral surrogates.
no code implementations • 1 Oct 2014 • Rafael M. Frongillo, Mark D. Reid
We introduce a new framework to model interactions among agents which seek to trade to minimize their risk with respect to some future outcome.
no code implementations • 24 Jun 2014 • Mark D. Reid, Rafael M. Frongillo, Robert C. Williamson, Nishant Mehta
Mixability is a property of a loss which characterizes when fast convergence is possible in the game of prediction with expert advice.
no code implementations • 10 Mar 2014 • Mark D. Reid, Rafael M. Frongillo, Robert C. Williamson
Mixability of a loss is known to characterise when constant regret bounds are achievable in games of prediction with expert advice through the use of Vovk's aggregating algorithm.
no code implementations • NeurIPS 2012 • Rafael M. Frongillo, Nicholás Della Penna, Mark D. Reid
We strengthen recent connections between prediction markets and learning by showing that a natural class of market makers can be understood as performing stochastic mirror descent when trader demands are sequentially drawn from a fixed distribution.
no code implementations • NeurIPS 2011 • Jacob D. Abernethy, Rafael M. Frongillo
Machine Learning competitions such as the Netflix Prize have proven reasonably successful as a method of “crowdsourcing” prediction tasks.