Before revision:  We provide a }  O(d ^2) regret bound where d is the covering dimension of the hypothesis class.
After revision:  We provide a } In the contextual pricing problem a seller repeatedly obtains products described by an adversarially chosen feature vector in \mathbb{R O(d ^2) regret bound where d is the covering dimension of the hypothesis class.