  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 (Rubinstein and Weinberg, 2018) 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 principled 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.
