Learning algorithms for second-price auctions with reserve

Mehryar Mohri, Andrés Muñoz Medina

Research output: Contribution to journalArticle

Abstract

Second-price auctions with reserve play a critical role in the revenue of modern search engine and popular online sites since the revenue of these companies often directly depends on the outcome of such auctions. The choice of the reserve price is the main mechanism through which the auction revenue can be influenced in these electronic markets. We cast the problem of selecting the reserve price to optimize revenue as a learning problem and present a full theoretical analysis dealing with the complex properties of the corresponding loss function. We further give novel algorithms for solving this problem and report the results of several experiments in both synthetic and real-world data demonstrating their effectiveness.

Original languageEnglish (US)
JournalJournal of Machine Learning Research
Volume17
StatePublished - Apr 1 2016

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Auctions
Learning algorithms
Learning Algorithm
Loss Function
Search Engine
Theoretical Analysis
Search engines
Optimise
Electronics
Experiment
Industry
Experiments

Keywords

  • Auctions
  • Learning theory
  • Revenue optimization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

Cite this

Learning algorithms for second-price auctions with reserve. / Mohri, Mehryar; Muñoz Medina, Andrés.

In: Journal of Machine Learning Research, Vol. 17, 01.04.2016.

Research output: Contribution to journalArticle

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