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

Kexin Huang, Ziqian Chen, Xue Wang, Chongming Gao, Jinyang Gao, Bolin Ding, Xiang Wang
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:19635-19659, 2024.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-huang24c, title = {Auctionformer: A Unified Deep Learning Algorithm for Solving Equilibrium Strategies in Auction Games}, author = {Huang, Kexin and Chen, Ziqian and Wang, Xue and Gao, Chongming and Gao, Jinyang and Ding, Bolin and Wang, Xiang}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {19635--19659}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/huang24c/huang24c.pdf}, url = {https://proceedings.mlr.press/v235/huang24c.html}, 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.} }
Endnote
%0 Conference Paper %T Auctionformer: A Unified Deep Learning Algorithm for Solving Equilibrium Strategies in Auction Games %A Kexin Huang %A Ziqian Chen %A Xue Wang %A Chongming Gao %A Jinyang Gao %A Bolin Ding %A Xiang Wang %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-huang24c %I PMLR %P 19635--19659 %U https://proceedings.mlr.press/v235/huang24c.html %V 235 %X 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.
APA
Huang, K., Chen, Z., Wang, X., Gao, C., Gao, J., Ding, B. & Wang, X.. (2024). Auctionformer: A Unified Deep Learning Algorithm for Solving Equilibrium Strategies in Auction Games. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:19635-19659 Available from https://proceedings.mlr.press/v235/huang24c.html.

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