Friedrich Dörmann, Osvald Frisk, Lars Nørvang Andersen, Christian Fischer Pedersen. Not All Noise is Accounted Equally: How Differentially Private Learning Benefits from Large Sampling Rates. In 31st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2021, Gold Coast, Australia, October 25-28, 2021. pages 1-6, IEEE, 2021. [doi]
@inproceedings{DormannFAP21,
title = {Not All Noise is Accounted Equally: How Differentially Private Learning Benefits from Large Sampling Rates},
author = {Friedrich Dörmann and Osvald Frisk and Lars Nørvang Andersen and Christian Fischer Pedersen},
year = {2021},
doi = {10.1109/MLSP52302.2021.9596307},
url = {https://doi.org/10.1109/MLSP52302.2021.9596307},
researchr = {https://researchr.org/publication/DormannFAP21},
cites = {0},
citedby = {0},
pages = {1-6},
booktitle = {31st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2021, Gold Coast, Australia, October 25-28, 2021},
publisher = {IEEE},
isbn = {978-1-7281-6338-3},
}