2019-06-25-bubeck19a.md

abstract	section	title	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
We propose a near-optimal method for highly smooth convex optimization. More precisely, in the oracle model where one obtains the $p^{th}$ order Taylor expansion of a function at the query point, we propose a method with rate of convergence $\tilde{O}(1/k^{\frac{ 3p +1}{2}})$ after $k$ queries to the oracle for any convex function whose $p^{th}$ order derivative is Lipschitz.	contributed	Near-optimal method for highly smooth convex optimization	inproceedings	Proceedings of Machine Learning Research	bubeck19a	0	Near-optimal method for highly smooth convex optimization	492	507	492-507	492	false	Bubeck, S{\'e}bastien and Jiang, Qijia and Lee, Yin Tat and Li, Yuanzhi and Sidford, Aaron	given family Sébastien Bubeck given family Qijia Jiang given family Yin Tat Lee given family Yuanzhi Li given family Aaron Sidford	2019-06-25		PMLR	Proceedings of the Thirty-Second Conference on Learning Theory	99	inproceedings	date-parts 2019 6 25	http://proceedings.mlr.press/v99/bubeck19a/bubeck19a.pdf