Constant Regret, Generalized Mixability, and Mirror Descent

We consider the setting of prediction with expert advice; a learner makes predictions by aggregating those of a group of experts. Under this setting, and for the right choice of loss function and "mixing" algorithm, it is possible for the learner to achieve a constant regret regardless of the number of prediction rounds. For example, a constant regret can be achieved for mixable losses using the aggregating algorithm. The Generalized Aggregating Algorithm (GAA) is a name for a family of algorithms parameterized by convex functions on simplices (entropies), which reduce to the aggregating algorithm when using the Shannon entropy operatornameS. For a given entropy Phi, losses for which a constant regret is possible using the textscGAA are called Phi-mixable. Which losses are Phi-mixable was previously left as an open question. We fully characterize Phi-mixability and answer other open questions posed by citeReid2015. We show that the Shannon entropy operatornameS is fundamental in nature when it comes to mixability; any Phi-mixable loss is necessarily operatornameS-mixable, and the lowest worst-case regret of the textscGAA is achieved using the Shannon entropy. Finally, by leveraging the connection between the mirror descent algorithm and the update step of the GAA, we suggest a new adaptive generalized aggregating algorithm and analyze its performance in terms of the regret bound.