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Benefits of Permutation-Equivariance in Auction Mechanisms
Tian Qin · Fengxiang He · Dingfeng Shi · Wenbing Huang · Dacheng Tao

Wed Nov 30 09:00 AM -- 11:00 AM (PST) @ Hall J #602

Designing an incentive-compatible auction mechanism that maximizes the auctioneer's revenue while minimizes the bidders’ ex-post regret is an important yet intricate problem in economics. Remarkable progress has been achieved through learning the optimal auction mechanism by neural networks. In this paper, we consider the popular additive valuation and symmetric valuation setting; i.e., the valuation for a set of items is defined as the sum of all items’ valuations in the set, and the valuation distribution is invariant when the bidders and/or the items are permutated. We prove that permutation-equivariant neural networks have significant advantages: the permutation-equivariance decreases the expected ex-post regret, improves the model generalizability, while maintains the expected revenue invariant. This implies that the permutation-equivariance helps approach the theoretically optimal dominant strategy incentive compatible condition, and reduces the required sample complexity for desired generalization. Extensive experiments fully support our theory. To our best knowledge, this is the first work towards understanding the benefits of permutation-equivariance in auction mechanisms.

Author Information

Tian Qin (University of Science and Technology of China)
Fengxiang He (JD.com Inc)

Fengxiang He received his BSc in statistics from University of Science and Technology of China, MPhil and PhD in computer science from the University of Sydney. He is currently an algorithm scientist at JD Explore Academy, JD.com Inc., leading its trustworthy AI team. His research interest is in the theory and practice of trustworthy AI, including deep learning theory, privacy-preserving ML, decentralized learning, and their applications. He has published in prominent journals and conferences, including TNNLS, TMM, TCSVT, ICML, NeurIPS, ICLR, CVPR, and ICCV. He is the area chair of AISTATS, BMVC, and ACML. He is the leading author of several standards on trustworthy AI.

Dingfeng Shi (Beijing University of Aeronautics and Astronautics)
Wenbing Huang (Tsinghua University)
Dacheng Tao (University of Technology, Sydney)

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