Article

Applying learning algorithms to preference elicitation

Authors:

Sebastien M. Lahaie,

David C. ParkesAuthors Info & Claims

EC '04: Proceedings of the 5th ACM conference on Electronic commerce

Pages 180 - 188

https://doi.org/10.1145/988772.988800

Published: 17 May 2004 Publication History

Abstract

We consider the parallels between the preference elicitation problem in combinatorial auctions and the problem of learning an unknown function from learning theory. We show that learning algorithms can be used as a basis for preference elicitation algorithms. The resulting elicitation algorithms perform a polynomial number of queries. We also give conditions under which the resulting algorithms have polynomial communication. Our conversion procedure allows us to generate combinatorial auction protocols from learning algorithms for polynomials, monotone DNF, and linear-threshold functions. In particular, we obtain an algorithm that elicits XOR bids with polynomial communication.

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Cited By

Birmpas GGan JHollender AMarmolejo-Cossío FRajgopal NVoudouris A(2022)Optimally Deceiving a Learning Leader in Stackelberg GamesJournal of Artificial Intelligence Research10.1613/jair.1.1254272(507-531)Online publication date: 4-Jan-2022
https://dl.acm.org/doi/10.1613/jair.1.12542
Lock EGoldberg PMarmolejo-Cossío F(2022)Learning Strong Substitutes Demand via QueriesACM Transactions on Economics and Computation10.1145/354660410:2(1-22)Online publication date: 7-Oct-2022
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Finocchiaro JMaio RMonachou FPatro GRaghavan MStoica ATsirtsis S(2021)Bridging Machine Learning and Mechanism Design towards Algorithmic FairnessProceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency10.1145/3442188.3445912(489-503)Online publication date: 3-Mar-2021
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Applying learning algorithms to preference elicitation

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Published In

cover image ACM Conferences

EC '04: Proceedings of the 5th ACM conference on Electronic commerce

May 2004

278 pages

ISBN:1581137710

DOI:10.1145/988772

General Chair:
Jack Breese
Microsoft
,
Program Chairs:
Joan Feigenbaum
Yale University
,
Margo Seltzer
Harvard University

Copyright © 2004 ACM.

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Published: 17 May 2004

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EC04

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EC04: Fifth ACM Conference on Electronic Commerce 2004 ( held in conjunction with WWW'04: The 2004 World Wide Web Conference )

May 17 - 20, 2004

NY, New York, USA

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EC '04 Paper Acceptance Rate 24 of 146 submissions, 16%;

Overall Acceptance Rate 664 of 2,389 submissions, 28%

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Cited By

Birmpas GGan JHollender AMarmolejo-Cossío FRajgopal NVoudouris A(2022)Optimally Deceiving a Learning Leader in Stackelberg GamesJournal of Artificial Intelligence Research10.1613/jair.1.1254272(507-531)Online publication date: 4-Jan-2022
https://dl.acm.org/doi/10.1613/jair.1.12542
Lock EGoldberg PMarmolejo-Cossío F(2022)Learning Strong Substitutes Demand via QueriesACM Transactions on Economics and Computation10.1145/354660410:2(1-22)Online publication date: 7-Oct-2022
https://dl.acm.org/doi/10.1145/3546604
Finocchiaro JMaio RMonachou FPatro GRaghavan MStoica ATsirtsis S(2021)Bridging Machine Learning and Mechanism Design towards Algorithmic FairnessProceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency10.1145/3442188.3445912(489-503)Online publication date: 3-Mar-2021
https://dl.acm.org/doi/10.1145/3442188.3445912
Zhang HConitzer VDaumé HSingh A(2020)Learning the valuations of a k-demand agentProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525964(11066-11075)Online publication date: 13-Jul-2020
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Goldberg PLock EMarmolejo-Cossío F(2020)Learning Strong Substitutes Demand via QueriesWeb and Internet Economics10.1007/978-3-030-64946-3_28(401-415)Online publication date: 6-Dec-2020
https://doi.org/10.1007/978-3-030-64946-3_28
Brero GLahaie SSeuken S(2019)Fast iterative combinatorial auctions via Bayesian learningProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v33i01.33011820(1820-1828)Online publication date: 27-Jan-2019
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Alanazi EMouhoub MZilles S(2019)The complexity of exact learning of acyclic conditional preference networks from swap examplesArtificial Intelligence10.1016/j.artint.2019.103182(103182)Online publication date: Oct-2019
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Brero GLubin BSeuken S(2018)Combinatorial auctions via machine learning-based preference elicitationProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304415.3304435(128-136)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304415.3304435
Brero GLubin BSeuken SSingh SMarkovitch S(2017)Probably approximately efficient combinatorial auctions via machine learningProceedings of the Thirty-First AAAI Conference on Artificial Intelligence10.5555/3298239.3298300(397-405)Online publication date: 4-Feb-2017
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Light AFrauenberger CPreece JStrohmeier PFerrario M(2017)Special Topic: Taking Action in a Changing WorldInteractions10.1145/316912825:1(34-45)Online publication date: 21-Dec-2017
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