Open Problem: Optimal Rates for Stochastic Decision-Theoretic Online Learning Under Differentially Privacy
Abstract
For the stochastic variant of decision-theoretic online learning with $K$ actions, $T$ rounds, and minimum gap $\Delta_{\min}$, the optimal, gap-dependent rate of the pseudo-regret is known to be $O \left( \frac{\log K}{\Delta_{\min}} \right)$. We ask to settle the optimal gap-dependent rate for the problem under $\varepsilon$-differential privacy.
Cite
Text
Hu and Mehta. "Open Problem: Optimal Rates for Stochastic Decision-Theoretic Online Learning Under Differentially Privacy." Conference on Learning Theory, 2024.Markdown
[Hu and Mehta. "Open Problem: Optimal Rates for Stochastic Decision-Theoretic Online Learning Under Differentially Privacy." Conference on Learning Theory, 2024.](https://mlanthology.org/colt/2024/hu2024colt-open/)BibTeX
@inproceedings{hu2024colt-open,
title = {{Open Problem: Optimal Rates for Stochastic Decision-Theoretic Online Learning Under Differentially Privacy}},
author = {Hu, Bingshan and Mehta, Nishant A.},
booktitle = {Conference on Learning Theory},
year = {2024},
pages = {5330-5334},
volume = {247},
url = {https://mlanthology.org/colt/2024/hu2024colt-open/}
}