2024-06-30-hu24a.md

title	section	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Open Problem: Optimal Rates for Stochastic Decision-Theoretic Online Learning Under Differentially Privacy	Open Problems	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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	hu24a	0	Open Problem: Optimal Rates for Stochastic Decision-Theoretic Online Learning Under Differentially Privacy	5330	5334	5330-5334	5330	false	Hu, Bingshan and Mehta, Nishant A.	given family Bingshan Hu given family Nishant A. Mehta	2024-06-30		Proceedings of Thirty Seventh Conference on Learning Theory	247	inproceedings	date-parts 2024 6 30	https://proceedings.mlr.press/v247/hu24a/hu24a.pdf