11 citations · 50 across the 10 of their papers we have counts for
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cs.GT2021★ 3 cited
Learning Revenue-Maximizing Auctions With Differentiable Matching
Michael J. Curry, Uro Lyi, Tom Goldstein +1
We propose a new architecture to approximately learn incentive compatible, revenue-maximizing auctions from sampled valuations. Our architecture uses the Sinkhorn algorithm to perf…
cs.GT2021★ 8 cited
PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning
Neehar Peri, Michael J. Curry, Samuel Dooley +1
The design of optimal auctions is a problem of interest in economics, game theory and computer science. Despite decades of effort, strategyproof, revenue-maximizing auction designs…