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Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation.
Robert
E. Schapire, Peter Stone, David
McAllester, Michael L. Littman, and János
A. Csirik.
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>},
}
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