• Classified by Topic • Classified by Publication Type • Sorted by Date • Sorted by First Author Last Name • Classified by Funding Source •

Robert E. Schapire, Peter Stone, David McAllester, Michael L. Littman,
and János A. Csirik. **Modeling Auction Price Uncertainty Using Boosting-based
Conditional Density Estimation**. In *Proceedings of the Nineteenth International Conference on Machine Learning*,
2002.

ICML-2002

[PDF]159.5kB [postscript]137.2kB

In complicated, interacting auctions, a fundamental problem is the prediction of prices of goods in the auctions, and more broadly, the modeling of uncertainty regarding these prices. In this paper, we present a machine-learning approach to this problem. The technique is based on a new and general boosting-based algorithm for conditional density estimation problems of this kind, i.e., supervised learning problems in which the goal is to estimate the entire conditional distribution of the real-valued label. This algorithm, which we present in detail, is at the heart of ATTac-2001, a top-scoring agent in the recent Trading Agent Competition (TAC-01). We describe how ATTac-2001 works, the results of the competition, and controlled experiments evaluating the effectiveness of price prediction in auctions.

@InProceedings{ICML02-tac, author = "Robert E. Schapire and Peter Stone and David Mc{A}llester and Michael L. Littman and J\'{a}nos A. Csirik", title = "Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation", booktitle = "Proceedings of the Nineteenth International Conference on Machine Learning", year = "2002", abstract={ In complicated, interacting auctions, a fundamental problem is the prediction of prices of goods in the auctions, and more broadly, the modeling of uncertainty regarding these prices. In this paper, we present a machine-learning approach to this problem. The technique is based on a new and general boosting-based algorithm for conditional density estimation problems of this kind, i.e., supervised learning problems in which the goal is to estimate the entire conditional distribution of the real-valued label. This algorithm, which we present in detail, is at the heart of ATTac-2001, a top-scoring agent in the recent Trading Agent Competition (TAC-01). We describe how ATTac-2001 works, the results of the competition, and controlled experiments evaluating the effectiveness of price prediction in auctions. }, wwwnote={<a href="http://www.cse.unsw.edu.au/~icml2002/">ICML-2002</a>}, }

Generated by bib2html.pl (written by Patrick Riley ) on Thu Mar 30, 2017 21:12:10