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

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

URL: http://arxiv.org/abs/2104.00159v1
Date: Wed, 31 Mar 2021 23:22:55 GMT
Title: Towards Prior-Free Approximately Truthful One-Shot Auction Learning via Differential Privacy
Authors: Daniel Reusche, Nicol\'as Della Penna
Abstract summary: deep learning techniques to find multi-item auctions in the prior-dependent setting. We modify the RegretNet approach to be applicable to the prior-free setting. Preliminary empirical results and qualitative analysis are presented.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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