Not All Learnable Distribution Classes Are Privately Learnable
Abstract
We give an example of a class of distributions that is learnable in total variation distance with a finite number of samples, but not learnable under $(\varepsilon, \delta)$-differential privacy. This refutes a conjecture of Ashtiani.
Cite
Text
Bun et al. "Not All Learnable Distribution Classes Are Privately Learnable." Proceedings of The 35th International Conference on Algorithmic Learning Theory, 2024.Markdown
[Bun et al. "Not All Learnable Distribution Classes Are Privately Learnable." Proceedings of The 35th International Conference on Algorithmic Learning Theory, 2024.](https://mlanthology.org/alt/2024/bun2024alt-all/)BibTeX
@inproceedings{bun2024alt-all,
title = {{Not All Learnable Distribution Classes Are Privately Learnable}},
author = {Bun, Mark and Kamath, Gautam and Mouzakis, Argyris and Singhal, Vikrant},
booktitle = {Proceedings of The 35th International Conference on Algorithmic Learning Theory},
year = {2024},
pages = {390-401},
volume = {237},
url = {https://mlanthology.org/alt/2024/bun2024alt-all/}
}