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Designing truthful, revenue maximizing auctions is a core problem of auctiondesign. Multi-item settings have long been elusive. Recent work(arXiv:1706.03459) introduces effective deep learning techniques to find suchauctions for the prior-dependent setting, in which distributions about bidderpreferences are known. One remaining problem is to obtain priors in a way thatexcludes the possibility of manipulating the resulting auctions. Usingtechniques from differential privacy for the construction of approximatelytruthful mechanisms, we modify the RegretNet approach to be applicable to theprior-free setting. In this more general setting, no distributional informationis assumed, but we trade this property for worse performance. We presentpreliminary empirical results and qualitative analysis for this work inprogress.

Towards Prior-Free Approximately Truthful One-Shot Auction Learning via Differential Privacy