Optimizing Your Online-Advertisement Asynchronously

We consider the problem of designing optimal online-ad investment strategies for a single advertiser, who invests at multiple sponsored search sites simultaneously, with the objective of maximizing his average revenue subject to the advertising budget constraint. A greedy online investment scheme is developed to achieve an average revenue that can be pushed to within $O(\epsilon)$ of the optimal, for any $\epsilon>0$, with a tradeoff that the temporal budget violation is $O(1/\epsilon)$. Different from many existing algorithms, our scheme allows the advertiser to \emph{asynchronously} update his investments on each search engine site, hence applies to systems where the timescales of action update intervals are heterogeneous for different sites. We also quantify the impact of inaccurate estimation of the system dynamics and show that the algorithm is robust against imperfect system knowledge

Bidding with limited statistical knowledge in online auctions. W-PIN+NetEcon: The joint

ABSTRACT We consider online auctions from the point of view of a single bidder who has an average budget constraint. By modeling the rest of the bidders through a probability distribution (often referred to as the mean-field approximation), we develop a simple bidding strategy which can be implemented without any statistical knowledge of bids, valuations, and query arrival processes. The key idea is to use stochastic approximation techniques to automatically track long-term averages