• Documents
  • Authors
  • Tables
  • Other Seers ▼
    RefSeer AckSeer CollabSeer SeerSeer
  • Log in
  • Sign up
  • MetaCart

CiteSeerX logo

Advanced Search Include Citations
Advanced Search Include Citations | Disambiguate

Online Learning in Online Auctions (2003)

Cached

  • Download as a PDF

Download Links

  • [www.cs.utexas.edu]
  • [www.cse.buffalo.edu]
  • [www.cs.washington.edu]
  • [www-2.cs.cmu.edu]
  • [www.cs.utexas.edu]
  • [www-2.cs.cmu.edu]
  • [www.lb.cs.cmu.edu]
  • [www-cgi.cs.cmu.edu]
  • [www-cgi.cs.cmu.edu.]
  • [www-cgi.cs.cmu.edu]
  • [www-cgi.cs.cmu.edu]

  • Other Repositories/Bibliography

  • DBLP
  • Save to List
  • Add to Collection
  • Correct Errors
  • Monitor Changes
by Avrim Blum , Vijay Kumar , Atri Rudra , Felix Wu
Citations:50 - 4 self
  • Summary
  • Active Bibliography
  • Co-citation
  • Clustered Documents
  • Version History

BibTeX

@MISC{Blum03onlinelearning,
    author = {Avrim Blum and Vijay Kumar and Atri Rudra and Felix Wu},
    title = {Online Learning in Online Auctions},
    year = {2003}
}

Years of Citing Articles

Bookmark

citeulike Connotea Bibsonomy Del.icio.us Digg Reddit

OpenURL

 

Abstract

ding truthfully and setting b i = v i . As shown in that paper, this condition # Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA, Email: avrim@cs.cmu.edu + Strategic Planning and Optimization Team, Amazon.com, Seattle, WA, Email: vijayk@amazon.com # Department of Computer Science, University of Texas at Austin, Austin, TX. This work was done while the author was at IBM India Research Lab, New Delhi, India. Email: atri@cs.utexas.edu Computer Science Division, University of California at Berkeley, Berkeley, CA, Email: felix@cs.berkeley.edu is equivalent to the condition that each s i depends only on the first i 1 bids, and not on the ith bid. Hence, the auction mechanism is essentially trying to guess the ith valuation, based on the first i 1 valuations. As in previous papers [3, 5, 6], we will use competitive analysis to analyze the performance of any given auction. Hence, we are interested in the worst-case ratio (over all sequences of valuations)

Citations

1714 Schapire R: A decision-theoretic generalization of online learning and an application to boosting - Freund - 1997
556 The weighted majority algorithm - Littlestone, Warmuth - 1994
267 How to use expert advice - Cesa-Bianchi, Freund, et al. - 1997
204 The nonstochastic multiarmed bandit problem - Auer, Cesa-Bianchi, et al.
144 Gambling in a rigged casino: the adversarial multiarmed bandit problem - Auer, Cesa-Bianchi, et al. - 1995
117 Game theory, On-line Prediction and Boosting - Freund, Schapire - 1996
113 Competitive auctions and digital goods - Goldberg, Hartline, et al.
81 Competitive generalized auctions - Fiat, Goldberg, et al. - 2002
80 Competitive analysis of incentive compatible on-line auctions - Lavi, Nisan
45 Incentive-compatible online auctions for digital goods - Bar-Yossef, Hildrum, et al. - 2002
9 Seller-focused algorithms for online auctioning - Bagchi, Chaudhary, et al. - 2001
1 Dynamic posted price mechnisms - Hartline - 2002
The National Science Foundation
  • About CiteSeerX
  • Submit Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2010 The Pennsylvania State University