Learning to bid in revenue-maximizing auctions - Archive ouverte HAL
Communication Dans Un Congrès Année : 2019

Learning to bid in revenue-maximizing auctions

Résumé

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.

Dates et versions

hal-03089621 , version 1 (28-12-2020)

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Thomas Nedelec, Noureddine El Karoui, Vianney Perchet. Learning to bid in revenue-maximizing auctions. 36 th International Conference on Machine Learning, 2019, Long Beach, France. ⟨hal-03089621⟩
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