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A Context-Integrated Transformer-Based Neural Network for Auction Design
Zhijian Duan · Jingwu Tang · Yutong Yin · Zhe Feng · Xiang Yan · Manzil Zaheer · Xiaotie Deng

Thu Jul 21 03:00 PM -- 05:00 PM (PDT) @ Hall E #133
One of the central problems in auction design is developing an incentive-compatible mechanism that maximizes the auctioneer's expected revenue. While theoretical approaches have encountered bottlenecks in multi-item auctions, recently, there has been much progress on finding the optimal mechanism through deep learning. However, these works either focus on a fixed set of bidders and items, or restrict the auction to be symmetric. In this work, we overcome such limitations by factoring \emph{public} contextual information of bidders and items into the auction learning framework. We propose $\mathtt{CITransNet}$, a context-integrated transformer-based neural network for optimal auction design, which maintains permutation-equivariance over bids and contexts while being able to find asymmetric solutions. We show by extensive experiments that $\mathtt{CITransNet}$ can recover the known optimal solutions in single-item settings, outperform strong baselines in multi-item auctions, and generalize well to cases other than those in training.

Author Information

Zhijian Duan (Peking University)
Jingwu Tang (Peking University)
Yutong Yin (Peking University)
Zhe Feng (Google Inc.)
Xiang Yan (Shanghai Jiao Tong University)
Manzil Zaheer (Google Research)
Xiaotie Deng (Peking University)

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