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- W3172339164 abstract "We propose a new architecture to approximately learn incentive compatible, revenue-maximizing auctions from sampled valuations. Our architecture uses the Sinkhorn algorithm to perform a differentiable bipartite matching which 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." @default.
- W3172339164 created "2021-06-22" @default.
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- W3172339164 date "2021-06-15" @default.
- W3172339164 modified "2023-09-27" @default.
- W3172339164 title "Learning Revenue-Maximizing Auctions With Differentiable Matching." @default.
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