Learning to bid in revenue-maximizing auctions

We consider the problem of the optimization of bidding strategies in prior-dependent revenuemaximizing 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 revenuemaximizing mechanisms. The new strategies we derive yield massive uplifts compared to the traditional truthfully bidding strategy.