Learning to bid in revenue-maximizing auctions

@InProceedings{pmlr-v97-nedelec19a,
  title = 	 {Learning to bid in revenue-maximizing auctions},
  author =       {Nedelec, Thomas and Karoui, Noureddine El and Perchet, Vianney},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {4781--4789},
  year = 	 {2019},
  editor = 	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  volume = 	 {97},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {09--15 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v97/nedelec19a/nedelec19a.pdf},
  url = 	 {https://proceedings.mlr.press/v97/nedelec19a.html},
  abstract = 	 {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.}
}