  This work explores adaptations of successful multi-armed bandits policies to
the online contextual bandits scenario with binary rewards using binary
classification algorithms such as logistic regression as black-box oracles.
Some of these adaptations are achieved through bootstrapping or approximate
bootstrapping, while others rely on other forms of randomness, resulting in
more scalable approaches than previous works, and the ability to work with any
type of classification algorithm. In particular, the Adaptive-Greedy algorithm
shows a lot of promise, in many cases achieving better performance than upper
confidence bound and Thompson sampling strategies, at the expense of more
hyperparameters to tune.
