  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 $\mathcal{F}$. We design a
fast and simple algorithm that achieves the statistically optimal regret with
only ${O}(\log T)$ calls to an offline 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 the contextual bandit literature. 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.
