  We consider the problem of contextual multi-armed bandits in the setting of
hypothesis transfer learning. That is, we assume having access to a previously
learned model on an unobserved set of contexts, and we leverage it in order to
accelerate exploration on a new bandit problem. Our transfer strategy is based
on a re-weighting scheme for which we show a reduction in the regret over the
classic Linear UCB when transfer is desired, while recovering the classic
regret rate when the two tasks are unrelated. We further extend this method to
an arbitrary amount of source models, where the algorithm decides which model
is preferred at each time step. Additionally we discuss an approach where a
dynamic convex combination of source models is given in terms of a biased
regularization term in the classic LinUCB algorithm. The algorithms and the
theoretical analysis of our proposed methods substantiated by empirical
evaluations on simulated and real-world data.
