Optimal regret minimization in posted-price auctions with strategic buyers

Mehryar Mohri, Andres Muñoz Medina

Research output: Contribution to journalConference articlepeer-review


We study revenue optimization learning algorithms for posted-price auctions with strategic buyers. We analyze a very broad family of monotone regret minimization algorithms for this problem, which includes the previously best known algorithm, and show that no algorithm in that family admits a strategic regret more favorable than Ω(√T). We then introduce a new algorithm that achieves a strategic regret differing from the lower bound only by a factor in O(logT), an exponential improvement upon the previous best algorithm. Our new algorithm admits a natural analysis and simpler proofs, and the ideas behind its design are general. We also report the results of empirical evaluations comparing our algorithm with the previous state of the art and show a consistent exponential improvement in several different scenarios.

Original languageEnglish (US)
Pages (from-to)1871-1879
Number of pages9
JournalAdvances in Neural Information Processing Systems
Issue numberJanuary
StatePublished - 2014
Event28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014 - Montreal, Canada
Duration: Dec 8 2014Dec 13 2014

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


Dive into the research topics of 'Optimal regret minimization in posted-price auctions with strategic buyers'. Together they form a unique fingerprint.

Cite this