Open Problem: Do You Pay for Privacy in Online Learning?
Abstract
Online learning, in the mistake bound model, is one of the most fundamental concepts in learning theory and differential privacy is, perhaps, the most widely used statistical concept of privacy in the machine learning community. Thus, defining problems which are online differentially privately learnable is of great interest in learning theory. In this paper, we pose the question on if the two problems are equivalent from a learning perspective, i.e., is privacy for free in the online learning framework?
Cite
Text
Sanyal and Ramponi. "Open Problem: Do You Pay for Privacy in Online Learning?." Conference on Learning Theory, 2022.Markdown
[Sanyal and Ramponi. "Open Problem: Do You Pay for Privacy in Online Learning?." Conference on Learning Theory, 2022.](https://mlanthology.org/colt/2022/sanyal2022colt-open/)BibTeX
@inproceedings{sanyal2022colt-open,
title = {{Open Problem: Do You Pay for Privacy in Online Learning?}},
author = {Sanyal, Amartya and Ramponi, Giorgia},
booktitle = {Conference on Learning Theory},
year = {2022},
pages = {5633-5637},
volume = {178},
url = {https://mlanthology.org/colt/2022/sanyal2022colt-open/}
}