Rate-adaptive model selection over a collection of black-box contextual bandit algorithms

Understand

We consider the model selection task in the stochastic contextual bandit setting.

• Suppose we are given a collection of base contextual bandit algorithms.
• We provide a master algorithm that combines them and achieves the same performance, up to constants, as the best base algorithm would, if it had been run on its own.
• Our approach only requires that each algorithm satisfy a high probability regret bound.