  Designing truthful, revenue maximizing auctions is a core problem of auction
design. Multi-item settings have long been elusive. Recent work
(arXiv:1706.03459) introduces effective deep learning techniques to find such
auctions for the prior-dependent setting, in which distributions about bidder
preferences are known. One remaining problem is to obtain priors in a way that
excludes the possibility of manipulating the resulting auctions. Using
techniques from differential privacy for the construction of approximately
truthful mechanisms, we modify the RegretNet approach to be applicable to the
prior-free setting. In this more general setting, no distributional information
is assumed, but we trade this property for worse performance. We present
preliminary empirical results and qualitative analysis for this work in
progress.
