Revenue optimization in AdExchange against strategic advertisers

In recent years, AdExchanges have become a popular tool for selling online advertisement space. This platform, has been beneficial not only to publishers, who have drastically increased their revenue by adopting it, but also to advertisers, who can design better marketing campaigns; and, ultimately to the user, who obtains a broader selection of ads relevant to his interests. Recently, some revenue optimization algorithms for AdExchanges have been proposed. However, none of these algorithms considers the possibility of facing a strategic advertiser. In this paper, the complications arising from these interactions are studied and a new revenue optimization algorithm is proposed and analyzed. In particular, we present tight bounds on the regret of an online learner attempting to optimize his revenue.