@COMMENT This file was generated by bib2html.pl version 0.90
@COMMENT written by Patrick Riley
@COMMENT This file came from Peter Stone's publication pages at
@COMMENT http://www.cs.utexas.edu/~pstone/papers
@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={ICML-2002},
}