  Designing an incentive compatible auction that maximizes expected revenue is
an intricate task. The single-item case was resolved in a seminal piece of work
by Myerson in 1981, but more than 40 years later a full analytical
understanding of the optimal design still remains elusive for settings with two
or more items. In this work, we initiate the exploration of the use of tools
from deep learning for the automated design of optimal auctions. We model an
auction as a multi-layer neural network, frame optimal auction design as a
constrained learning problem, and show how it can be solved using standard
machine learning pipelines. In addition to providing generalization bounds, we
present extensive experimental results, recovering essentially all known
solutions that come from the theoretical analysis of optimal auction design
problems and obtaining novel mechanisms for settings in which the optimal
mechanism is unknown.
