Learning to bid in revenue maximizing auction

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We consider the problem of the optimization of bidding strategies in prior-dependent revenue-maximizing auctions, when the seller fixes the reserve prices based on the bid distributions. Our study is done in the setting where one bidder is strategic. Using a variational approach, we study the complexity of the original objective and we introduce a relaxation of the objective functional in order to use gradient descent methods. Our approach is simple, general and can be applied to various value distributions and revenue-maximizing mechanisms. The new strategies we derive yield massive uplifts compared to the traditional truthfully bidding strategy.

Original languageEnglish
Title of host publicationThe Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019
PublisherAssociation for Computing Machinery, Inc
Pages934-935
Number of pages2
ISBN (Electronic)9781450366755
DOIs
Publication statusPublished - 13 May 2019
Externally publishedYes
Event2019 World Wide Web Conference, WWW 2019 - San Francisco, United States
Duration: 13 May 201917 May 2019

Publication series

NameThe Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019

Conference

Conference2019 World Wide Web Conference, WWW 2019
Country/TerritoryUnited States
CitySan Francisco
Period13/05/1917/05/19

Fingerprint

Dive into the research topics of 'Learning to bid in revenue maximizing auction'. Together they form a unique fingerprint.

Cite this