  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.
