Jad Rahme, Samy Jelassi, S. Matthew Weinberg

Research output: Contribution to conferencePaperpeer-review

17 Scopus citations


Designing an incentive compatible auction that maximizes expected revenue is a central problem in Auction Design. While theoretical approaches to the problem have hit some limits, a recent research direction initiated by Duetting et al. (2019) consists in building neural network architectures to find optimal auctions. We propose two conceptual deviations from their approach which result in enhanced performance. First, we use recent results in theoretical auction design to introduce a time-independent Lagrangian. This not only circumvents the need for an expensive hyper-parameter search (as in prior work), but also provides a single metric to compare the performance of two auctions (absent from prior work). Second,the optimization procedure in previous work uses an inner maximization loop to compute optimal misreports. We amortize this process through the introduction of an additional neural network. We demonstrate the effectiveness of our approach by learning competitive or strictly improved auctions compared to prior work. Both results together further imply a novel formulation of Auction Design as a two-player game with stationary utility functions.

Original languageEnglish (US)
StatePublished - 2021
Event9th International Conference on Learning Representations, ICLR 2021 - Virtual, Online
Duration: May 3 2021May 7 2021


Conference9th International Conference on Learning Representations, ICLR 2021
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Computer Science Applications
  • Education
  • Linguistics and Language


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