  We study a novel variant of the parameterized bandits problem in which the
learner can observe additional auxiliary feedback that is correlated with the
observed reward. The auxiliary feedback is readily available in many real-life
applications, e.g., an online platform that wants to recommend the best-rated
services to its users can observe the user's rating of service (rewards) and
collect additional information like service delivery time (auxiliary feedback).
In this paper, we first develop a method that exploits auxiliary feedback to
build a reward estimator with tight confidence bounds, leading to a smaller
regret. We then characterize the regret reduction in terms of the correlation
coefficient between reward and its auxiliary feedback. Experimental results in
different settings also verify the performance gain achieved by our proposed
method.

Machine Learning;Machine Learning