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| Type | Conference or Workshop Paper |
| Scope | Learning and pedagogical Research |
| Published in Proceedings | Yes |
| Title | Fourier Analysis-based Iterative Combinatorial Auctions |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Page Range | 549 - 556 |
| Event Title | Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22 |
| Event Type | conference |
| Event Location | Vienna, Austria |
| Event Start Date | July 23 - 2022 |
| Event End Date | July 29 - 2022 |
| Publisher | International Joint Conferences on Artificial Intelligence Organization |
| Abstract Text | Recent advances in Fourier analysis have brought new tools to efficiently represent and learn set functions. In this paper, we bring the power of Fourier analysis to the design of combinatorial auctions (CAs). The key idea is to approximate bidders' value functions using Fourier-sparse set functions, which can be computed using a relatively small number of queries. Since this number is still too large for practical CAs, we propose a new hybrid design: we first use neural networks (NNs) to learn bidders’ values and then apply Fourier analysis to the learned representations. On a technical level, we formulate a Fourier transform-based winner determination problem and derive its mixed integer program formulation. Based on this, we devise an iterative CA that asks Fourier-based queries. We experimentally show that our hybrid ICA achieves higher efficiency than prior auction designs, leads to a fairer distribution of social welfare, and significantly reduces runtime. With this paper, we are the first to leverage Fourier analysis in CA design and lay the foundation for future work in this area. Our code is available on GitHub: https://github.com/marketdesignresearch/FA-based-ICAs. |
| Free access at | Official URL |
| Official URL | https://arxiv.org/abs/2009.10749 |
| Related URLs | |
| Digital Object Identifier | 10.24963/ijcai.2022/78 |
| Other Identification Number | merlin-id:23342 |
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