Learning Theory and Algorithms for revenue optimization in second price auctions with reserve
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(1):262-270, 2014.
Second-price auctions with reserve play a critical role for modern search engine and popular online sites since the revenue of these companies often directly depends on the outcome of such auctions. The choice of the reserve price is the main mechanism through which the auction revenue can be influenced in these electronic markets. We cast the problem of selecting the reserve price to optimize revenue as a learning problem and present a full theoretical analysis dealing with the complex properties of the corresponding loss function (it is non-convex and discontinuous). We further give novel algorithms for solving this problem and report the results of encouraging experiments demonstrating their effectiveness.