Learning to Bid in Revenue-Maximizing Auctions
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.
Cite
Text
Nedelec et al. "Learning to Bid in Revenue-Maximizing Auctions." International Conference on Machine Learning, 2019.Markdown
[Nedelec et al. "Learning to Bid in Revenue-Maximizing Auctions." International Conference on Machine Learning, 2019.](https://mlanthology.org/icml/2019/nedelec2019icml-learning/)BibTeX
@inproceedings{nedelec2019icml-learning,
title = {{Learning to Bid in Revenue-Maximizing Auctions}},
author = {Nedelec, Thomas and El Karoui, Noureddine and Perchet, Vianney},
booktitle = {International Conference on Machine Learning},
year = {2019},
pages = {4781-4789},
volume = {97},
url = {https://mlanthology.org/icml/2019/nedelec2019icml-learning/}
}