1,660 research outputs found
Learning Theory and Algorithms for Revenue Optimization in Second-Price Auctions with Reserve
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 de- pends 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. We further give novel algorithms for solving this problem and report
the results of several experiments in both synthetic and real data
demonstrating their effectiveness.Comment: Accepted at ICML 201
Learning to bid in revenue-maximizing auctions
We consider the problem of the optimization of bidding strategies in
prior-dependent revenue-maximizing auctions, when the seller fixes the reserve
prices based on the bid distributions. Our study is done in the setting where
one bidder is strategic. Using a variational approach, we study the complexity
of the original objective and we introduce a relaxation of the objective
functional in order to use gradient descent methods. Our approach is simple,
general and can be applied to various value distributions and
revenue-maximizing mechanisms. The new strategies we derive yield massive
uplifts compared to the traditional truthfully bidding strategy
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