Auctionformer: A Unified Deep Learning Algorithm for Solving Equilibrium Strategies in Auction Games

Abstract

Auction games have been widely used in plenty of trading environments such as online advertising and real estate. The complexity of real-world scenarios, characterized by diverse auction mechanisms and bidder asymmetries, poses significant challenges in efficiently solving for equilibria. Traditional learning approaches often face limitations due to their specificity to certain settings and high resource demands. Addressing this, we introduce Auctionformer, an efficient transformer-based method to solve equilibria of diverse auctions in a unified framework. Leveraging the flexible tokenization schemes, Auctionformer translates varying auction games into a standard token series, making use of renowned Transformer architectures. Moreover, we employ Nash error as the loss term, sidestepping the need for underlying equilibrium solutions and enabling efficient training and inference. Furthermore, a few-shot framework supports adaptability to new mechanisms, reinforced by a self-supervised fine-tuning approach. Extensive experimental results affirm the superior performance of Auctionformer over contemporary methods, heralding its potential for broad real-world applications.

@inproceedings{Huangicml24,
  author       = {Kexin Huang and
                  Ziqian Chen and
                  Xue Wang and
                  Chongming Gao and
                  Jinyang Gao and
                  Bolin Ding and
                  Xiang Wang},
  title        = {Auctionformer: {A} Unified Deep Learning Algorithm for Solving Equilibrium
                  Strategies in Auction Games},
  booktitle    = {{ICML}},
  publisher    = {OpenReview.net},
  year         = {2024}
}

Kexin Huang

黄科鑫

Chongming Gao

高崇铭 博士后

Xiang Wang

王翔 教授