  Contextual bandit algorithms are essential for solving many real-world
interactive machine learning problems. Despite multiple recent successes on
statistically and computationally efficient methods, the practical behavior of
these algorithms is still poorly understood. We leverage the availability of
large numbers of supervised learning datasets to empirically evaluate
contextual bandit algorithms, focusing on practical methods that learn by
relying on optimization oracles from supervised learning. We find that a recent
method (Foster et al., 2018) using optimism under uncertainty works the best
overall. A surprisingly close second is a simple greedy baseline that only
explores implicitly through the diversity of contexts, followed by a variant of
Online Cover (Agarwal et al., 2014) which tends to be more conservative but
robust to problem specification by design. Along the way, we also evaluate
various components of contextual bandit algorithm design such as loss
estimators. Overall, this is a thorough study and review of contextual bandit
methodology.
