  Non-stationary bandits and online clustering of bandits lift the restrictive
assumptions in contextual bandits and provide solutions to many important
real-world scenarios. Though the essence in solving these two problems overlaps
considerably, they have been studied independently. In this paper, we connect
these two strands of bandit research under the notion of test of homogeneity,
which seamlessly addresses change detection for non-stationary bandit and
cluster identification for online clustering of bandit in a unified solution
framework. Rigorous regret analysis and extensive empirical evaluations
demonstrate the value of our proposed solution, especially its flexibility in
handling various environment assumptions.
