An optimal algorithm for on-line bipartite matching
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
Adaptive bidding for display advertising
Proceedings of the 18th international conference on World wide web
The adwords problem: online keyword matching with budgeted bidders under random permutations
Proceedings of the 10th ACM conference on Electronic commerce
Proceedings of the 5th International Workshop on Internet and Network Economics
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Bidding for Representative Allocations for Display Advertising
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Online Stochastic Matching: Beating 1-1/e
FOCS '09 Proceedings of the 2009 50th Annual IEEE Symposium on Foundations of Computer Science
Optimal online assignment with forecasts
Proceedings of the 11th ACM conference on Electronic commerce
Online stochastic packing applied to display ad allocation
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
Efficient online ad serving in a display advertising exchange
Proceedings of the fourth ACM international conference on Web search and data mining
Selective call out and real time bidding
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Bid landscape forecasting in online ad exchange marketplace
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Real-time bidding algorithms for performance-based display ad allocation
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Marketing campaign evaluation in targeted display advertising
Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy
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Most of the online advertising today is sold via an auction, which requires the advertiser to respond with a valid bid within a fraction of a second. As such, most advertisers employ bidding agents to submit bids on their behalf. The architecture of such agents typically has (1) an offline optimization phase which incorporates the bidder's knowledge about the market and (2) an online bidding strategy which simply executes the offline strategy. The online strategy is typically highly dependent on both supply and expected price distributions, both of which are forecast using traditional machine learning methods. In this work we investigate the optimum strategy of the bidding agent when faced with incorrect forecasts. At a high level, the agent can invest resources in improving the forecasts, or can tighten the loop between successive offline optimization cycles in order to detect errors more quickly. We show analytically that the latter strategy, while simple, is extremely effective in dealing with forecast errors, and confirm this finding with experimental evaluations.