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@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>},
}