  We provide a simple method to combine stochastic bandit algorithms. Our
approach is based on a "meta-UCB" procedure that treats each of $N$ individual
bandit algorithms as arms in a higher-level $N$-armed bandit problem that we
solve with a variant of the classic UCB algorithm. Our final regret depends
only on the regret of the base algorithm with the best regret in hindsight.
This approach provides an easy and intuitive alternative strategy to the CORRAL
algorithm for adversarial bandits, without requiring the stability conditions
imposed by CORRAL on the base algorithms. Our results match lower bounds in
several settings, and we provide empirical validation of our algorithm on
misspecified linear bandit and model selection problems.
