  In this paper we propose the Augmented-UCB (AugUCB) algorithm for a
fixed-budget version of the thresholding bandit problem (TBP), where the
objective is to identify a set of arms whose quality is above a threshold. A
key feature of AugUCB is that it uses both mean and variance estimates to
eliminate arms that have been sufficiently explored; to the best of our
knowledge this is the first algorithm to employ such an approach for the
considered TBP. Theoretically, we obtain an upper bound on the loss
(probability of mis-classification) incurred by AugUCB. Although UCBEV in
literature provides a better guarantee, it is important to emphasize that UCBEV
has access to problem complexity (whose computation requires arms' mean and
variances), and hence is not realistic in practice; this is in contrast to
AugUCB whose implementation does not require any such complexity inputs. We
conduct extensive simulation experiments to validate the performance of AugUCB.
Through our simulation work, we establish that AugUCB, owing to its utilization
of variance estimates, performs significantly better than the state-of-the-art
APT, CSAR and other non variance-based algorithms.
