  There are two variants of the classical multi-armed bandit (MAB) problem that
have received considerable attention from machine learning researchers in
recent years: contextual bandits and simple regret minimization. Contextual
bandits are a sub-class of MABs where, at every time step, the learner has
access to side information that is predictive of the best arm. Simple regret
minimization assumes that the learner only incurs regret after a pure
exploration phase. In this work, we study simple regret minimization for
contextual bandits. Motivated by applications where the learner has separate
training and autonomous modes, we assume that the learner experiences a pure
exploration phase, where feedback is received after every action but no regret
is incurred, followed by a pure exploitation phase in which regret is incurred
but there is no feedback. We present the Contextual-Gap algorithm and establish
performance guarantees on the simple regret, i.e., the regret during the pure
exploitation phase. Our experiments examine a novel application to adaptive
sensor selection for magnetic field estimation in interplanetary spacecraft,
and demonstrate considerable improvement over algorithms designed to minimize
the cumulative regret.
