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Learning Revenue-Maximizing Auctions With Differentiable Matching
Michael Curry · Uro Lyi · Tom Goldstein · John P Dickerson
Event URL: https://arxiv.org/pdf/2106.07877.pdf »

We propose a new architecture to approximately learn incentive compatible, revenue-maximizing auctions from sampled valuations, which uses the Sinkhorn algorithm to perform a differentiable bipartite matching. This new framework allows the network to learn strategyproof revenue-maximizing mechanisms in settings not learnable by the previous RegretNet architecture. In particular, our architecture is able to learn mechanisms in settings without free disposal where each bidder must be allocated exactly some number of items. In experiments, we show our approach successfully recovers multiple known optimal mechanisms and high-revenue, low-regret mechanisms in larger settings where the optimal mechanism is unknown.

Author Information

Michael Curry (University of Maryland)
Uro Lyi (University of Maryland, College Park)
Tom Goldstein (Rice University)
John P Dickerson (Carnegie Mellon)

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