# Generalized Mixability via Entropic Duality

Proceedings of The 28th Conference on Learning Theory,
pp. 1501–1522, 2015

## Abstract

Mixability is a property of a loss which characterizes when constant regret 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 so we introduce a more general notion of \(\Phi\)-mixability where \(\Phi\) is a general entropy (

*i.e.*, 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 which \(\Phi\) have non-trivial \(\Phi\)-mixable losses and relate \(\Phi\)-mixability and its associated Aggregating Algorithm to potential-based methods, a Blackwell-like condition, mirror descent, and risk measures from finance. We also define a notion of “dominance” between different entropies in terms of bounds they guarantee and conjecture that classical mixability gives optimal bounds, for which we provide some supporting empirical evidence.