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

arXiv: https://arxiv.org/abs/2104.00159 · HTML: https://mlsanity.com/paper/2104.00159/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 31 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization](https://mlsanity.com/paper/2010.09063/) (2020)
- [Auction learning as a two-player game](https://mlsanity.com/paper/2006.05684/) (2020)
- [Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms](https://mlsanity.com/paper/2007.09371/) (2020)

## Built on by (most cited first, 1 of 1)

- [Differential Privacy: What is all the noise about?](https://mlsanity.com/paper/2205.09453/) (2022)

## Abstract (arXiv)

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.