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

Peter Stone Faculty pstone [at] cs utexas edu