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We consider the general (stochastic) contextual bandit problem under the realizability assumption, i.e., the expected reward, as a function of contexts and actions, belongs to a general function class F. We design a fast and simple algorithm that achieves the statistically optimal regret with only O(log T) calls to an offline least-squares regression oracle across all T rounds. The number of oracle calls can be further reduced to O(log log T) if T is known in advance. Our results provide the first universal and optimal reduction from contextual bandits to offline regression, solving an important open problem in contextual bandits. A direct consequence of our results is that any advances in offline regression immediately translate to contextual bandits, statistically and computationally. This leads to faster algorithms and improved regret guarantees for broader classes of contextual bandit problems.

Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizability