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Online learning in online auctions
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Source Symposium on Discrete Algorithms archive
Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms table of contents
Baltimore, Maryland
SESSION: Session 4A table of contents
Pages: 202 - 204  
Year of Publication: 2003
ISBN:0-89871-538-5
Authors
Avrim Blum  Carnegie Mellon University, Pittsburgh, PA
Vijay Kumar  Amazon.com, Seattle, WA
Atri Rudra  University of Texas at Austin, Austin, TX
Felix Wu  University of California at Berkeley, Berkeley, CA
Sponsors
: SIAM Activity Group on Discrete Mathematics
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
Publisher
Society for Industrial and Applied Mathematics  Philadelphia, PA, USA
Bibliometrics
Downloads (6 Weeks): 6,   Downloads (12 Months): 64,   Citation Count: 19
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REFERENCES

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CITED BY  19


REVIEW

"Barrett Hazeltine : Reviewer"

Performance of an online auction can be improved by an algorithm that learns from the set of bids already made. An online auction receives bids, and deals with each individually, deciding whether to accept a bid or wait for a higher one. This pape  more...

Collaborative Colleagues:
Avrim Blum: colleagues
Vijay Kumar: colleagues
Atri Rudra: colleagues
Felix Wu: colleagues