A parameter-free hedging algorithm

Understand

We study the problem of decision-theoretic online learning (DTOL).

• Motivated by practical applications, we focus on DTOL when the number of actions is very large.
• Previous algorithms for learning in this framework have a tunable learning rate parameter, and a barrier to using online-learning in practical applications is that it is not understood how to set this parameter optimally, particularly when the number of actions is large.
• In this paper, we offer a clean solution by proposing a novel and completely parameter-free algorithm for DTOL.