We consider the adversarial linear contextual bandit problem, where the loss vectors are selected fully adversarially and the per-round action set (i.e. the context) is drawn from a fixed distribution. Existing methods for this problem either require access to a simulator to generate free i.i.d. contexts, achieve a sub-optimal regret no better than 𝒪̃(T^5/6), or are computationally inefficient. We greatly improve these results by achieving a regret of 𝒪̃(√(T)) without a simulator, while mainta…