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

Adapting multi-armed bandits policies to contextual bandits scenarios