TY - CONF ID - www200926 UR - http://www2009.eprints.org/26/ A1 - Ghosh, Arpita A1 - Rubinstein, Benjamin I. P. A1 - Vassilvitskii, Sergei A1 - Zinkevich, Martin Y1 - 2009/04// N2 - Motivated by the emergence of auction-based marketplaces for display ads such as the Right Media Exchange, we study the design of a bidding agent that implements a display advertising campaign by bidding in such a marketplace. The bidding agent must acquire a given number of impressions with a given target spend, when the highest external bid in the marketplace is drawn from an unknown distribution P . The quantity and spend constraints arise from the fact that display ads are usually sold on a CPM basis. We consider both the full information setting, where the winning price in each auction is announced publicly, and the partially observable setting where only the winner obtains information about the distribution; these differ in the penalty incurred by the agent while attempting to learn the distribution. We provide algorithms for both settings, and prove performance guarantees using bounds on uniform closeness from statistics, and techniques from online learning. We experimentally evaluate these algorithms: both algorithms perform very well with respect to both target quantity and spend; further, our algorithm for the partially observable case performs nearly as well as that for the fully observable setting despite the higher penalty incurred during learning. TI - Adaptive Bidding for Display Advertising SP - 251 M2 - Madrid, Spain AV - public EP - 251 T2 - 18th International World Wide Web Conference ER -