Online learning in online auctions
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A nonparametric estimator for setting: reserve prices in procurement auctions
Proceedings of the 4th ACM conference on Electronic commerce
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Adaptive mechanism design: a metalearning approach
ICEC '06 Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet
Competing sellers in online markets: reserve prices, shill bidding, and auction fees
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Bidding agents for online auctions with hidden bids
Machine Learning
Optimal design of english auctions with discrete bid levels
ACM Transactions on Internet Technology (TOIT)
Journal of Artificial Intelligence Research
Sellers competing for buyers in online markets: reserve prices, shill bids, and auction fees
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multi-armed bandit algorithms and empirical evaluation
ECML'05 Proceedings of the 16th European conference on Machine Learning
AMEC'05 Proceedings of the 2005 international conference on Agent-Mediated Electronic Commerce: designing Trading Agents and Mechanisms
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The success of an auction design often hinges on its ability to set parameters such as reserve price and bid levels that will maximize an objective function such as the auctioneer revenue. Works on designing adaptive auction mechanisms have emerged recently, and the challenge is in learning different auction parameters by observing the bidding in previous auctions. In this paper, we propose a non-parametric method for determining discrete bid levels dynamically so as to maximize the auctioneer revenue. First, we propose a non-parametric kernel method for estimating the probabilities of closing price with past auction data. Then a greedy strategy has been devised to determine the discrete bid levels based on the estimated probability information of closing price. We show experimentally that our non-parametric method is robust to changes in parameters such as the distributions of participating bidders as well as the individual bidder evaluation, and it consistently outperforms different competitors with various settings with respect to auctioneer revenue maximization.