Regret Minimization for Reserve Prices in Second-Price Auctions

Nicolò Cesa-Bianchi, Claudio Gentile, Yishay Mansour

Research output: Contribution to journalArticlepeer-review

Abstract

We show a regret minimization algorithm for setting the reserve price in a sequence of second-price auctions, under the assumption that all bids are independently drawn from the same unknown and arbitrary distribution. Our algorithm is computationally efficient, and achieves a regret of Q{script}(√T) in a sequence of T auctions. This holds even when the number of bidders is stochastic with a known distribution.

Original languageEnglish
Article number6939698
Pages (from-to)549-564
Number of pages16
JournalIEEE Transactions on Information Theory
Volume61
Issue number1
DOIs
StatePublished - 1 Jan 2015

Keywords

  • Prediction theory
  • semi-supervised learning
  • sequential analysis
  • statistical learning

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