We present an oracle-efficient relaxation for the adversarial contextual bandits problem, where the contexts are sequentially drawn i.i.d from a known distribution and the cost sequence is chosen by an online adversary. Our algorithm has a regret bound of $O(T^{\frac{2}{3}}(K\log(|\Pi|))^{\frac{1}{3}})$ and makes at most $O(K)$ calls per round to an offline optimization oracle, where $K$ denotes the number of actions, $T$ denotes the number of rounds and $\Pi$ denotes the set of policies. This is the first result to improve the prior best bound of $O((TK)^{\frac{2}{3}}(\log(|\Pi|))^{\frac{1}{3}})$ as obtained by Syrgkanis et al. at NeurIPS 2016, and the first to match the original bound of Langford and Zhang at NeurIPS 2007 which was obtained for the stochastic case.

翻译：我们提出了一种面向对抗性上下文赌博机问题的甲骨文高效松弛方法，其中上下文从已知分布中独立同分布地逐轮抽取，而代价序列由在线对手选择。我们的算法具有$O(T^{\frac{2}{3}}(K\log(|\Pi|))^{\frac{1}{3}})$的遗憾界，且每轮对离线优化甲骨的调用次数至多为$O(K)$，其中$K$表示动作数，$T$表示轮次数，$\Pi$表示策略集。这是首个将Syrgkanis等人（NeurIPS 2016）先前获得的$O((TK)^{\frac{2}{3}}(\log(|\Pi|))^{\frac{1}{3}})$最优界提升的结果，也是首个匹配Langford和Zhang（NeurIPS 2007）针对随机情形得到的原始界的结果。