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- W2953108712 abstract "Mixability is a property of a loss which characterizes when fast convergence is possible in the game of prediction with expert advice. We show that a key property of mixability generalizes, and the exp and log operations present in the usual theory are not as special as one might have thought. In doing this we introduce a more general notion of $Phi$-mixability where $Phi$ is a general entropy (ie, any convex function on probabilities). We show how a property shared by the convex dual of any such entropy yields a natural algorithm (the minimizer of a regret bound) which, analogous to the classical aggregating algorithm, is guaranteed a constant regret when used with $Phi$-mixable losses. We characterize precisely which $Phi$ have $Phi$-mixable losses and put forward a number of conjectures about the optimality and relationships between different choices of entropy." @default.
- W2953108712 created "2019-06-27" @default.
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- W2953108712 date "2014-06-24" @default.
- W2953108712 modified "2023-09-27" @default.
- W2953108712 title "Generalized Mixability via Entropic Duality" @default.
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