Privacy preserving auctions and mechanism design
Proceedings of the 1st ACM conference on Electronic commerce
Competitive auctions and digital goods
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
On approximating optimal auctions
Proceedings of the 3rd ACM conference on Electronic Commerce
Competitive generalized auctions
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Incentive-compatible online auctions for digital goods
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Online learning in online auctions
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
On the Hardness of Optimal Auctions
FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
The Value of Knowing a Demand Curve: Bounds on Regret for Online Posted-Price Auctions
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Adaptive limited-supply online auctions
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Computational issues in optimal auction design
Computational issues in optimal auction design
Mechanism Design via Machine Learning
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Secure Vickrey auctions without threshold trust
FC'02 Proceedings of the 6th international conference on Financial cryptography
Algorithmic pricing via virtual valuations
Proceedings of the 8th ACM conference on Electronic commerce
Bayesian optimal auctions via multi- to single-agent reduction
Proceedings of the 13th ACM Conference on Electronic Commerce
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A classical paper of Myerson [18] shows how to construct an optimal (revenue-maximizing) auction in a model where bidders' values are drawn from known continuous distributions. In this paper we show how to adapt this approach to finite support distributions that may be partially unknown. We demonstrate that a Myerson-style auction can be constructed in time polynomial in the number of bidders and the size of the support sets. Next, we consider the scenario where the mechanism designer knows the support sets, but not the probability of each value. In this situation, we show that the optimal auction may be learned in polynomial time using a weak oracle that, given two candidate auctions, returns one with a higher expected revenue. To study this problem, we introduce a new class of truthful mechanisms which we call order-based auctions. We show that the optimal mechanism is an order-based auction and use the internal structure of this class to prove the correctness of our learning algorithm as well as to bound its running time.