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Learning to bid in revenue-maximizing auctions
Thomas Nedelec · Noureddine El Karoui · Vianney Perchet

Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #157

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.

Author Information

Thomas Nedelec (ENS Paris Saclay - Criteo AI Lab)
Noureddine El Karoui (Criteo AI Lab and UC, Berkeley)
Vianney Perchet (ENS Paris Saclay & Criteo AI Lab)

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