Non-parametric revenue optimization for generalized second price auctions

Mehryar Mohri, Andrés Muñoz Medina

Research output: Contribution to conferencePaper

Abstract

We present an extensive analysis of the key problem of learning optimal reserve prices for generalized second price auctions. We describe two algorithms for this task: one based on density estimation, and a novel algorithm benefiting from solid theoretical guarantees and with a very favorable running-time complexity of O(nS log(nS)), where n is the sample size and S the number of slots. Our theoretical guarantees are more favorable than those previously presented in the literature. Additionally, we show that even if bidders do not play at an equilibrium, our second algorithm is still well defined and minimizes a quantity of interest. To our knowledge, this is the first attempt to apply learning algorithms to the problem of reserve price optimization in GSP auctions. Finally, we present the first convergence analysis of empirical equilibrium bidding functions to the unique symmetric Bayesian-Nash equilibrium of a GSP.

Original languageEnglish (US)
Pages612-621
Number of pages10
StatePublished - Jan 1 2015
Event31st Conference on Uncertainty in Artificial Intelligence, UAI 2015 - Amsterdam, Netherlands
Duration: Jul 12 2015Jul 16 2015

Other

Other31st Conference on Uncertainty in Artificial Intelligence, UAI 2015
CountryNetherlands
CityAmsterdam
Period7/12/157/16/15

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ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Mohri, M., & Medina, A. M. (2015). Non-parametric revenue optimization for generalized second price auctions. 612-621. Paper presented at 31st Conference on Uncertainty in Artificial Intelligence, UAI 2015, Amsterdam, Netherlands.