  We consider the stochastic multi-armed bandit problem and the contextual
bandit problem with historical observations and pre-clustered arms. The
historical observations can contain any number of instances for each arm, and
the pre-clustering information is a fixed clustering of arms provided as part
of the input. We develop a variety of algorithms which incorporate this offline
information effectively during the online exploration phase and derive their
regret bounds. In particular, we develop the META algorithm which effectively
hedges between two other algorithms: one which uses both historical
observations and clustering, and another which uses only the historical
observations. The former outperforms the latter when the clustering quality is
good, and vice-versa. Extensive experiments on synthetic and real world
datasets on Warafin drug dosage and web server selection for latency
minimization validate our theoretical insights and demonstrate that META is a
robust strategy for optimally exploiting the pre-clustering information.

Machine Learning;Machine Learning