Alexander Gasnikov, Pavel Dvurechensky, Eduard Gorbunov, Evgeniya Vorontsova, Daniil Selikhanovych, César A. Uribe, Bo Jiang 0007, Haoyue Wang, Shuzhong Zhang, Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford. Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives. In Alina Beygelzimer, Daniel Hsu 0001, editors, Conference on Learning Theory, COLT 2019, 25-28 June 2019, Phoenix, AZ, USA. Volume 99 of Proceedings of Machine Learning Research, pages 1392-1393, PMLR, 2019. [doi]
@inproceedings{GasnikovDGVSU0W19,
title = {Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives},
author = {Alexander Gasnikov and Pavel Dvurechensky and Eduard Gorbunov and Evgeniya Vorontsova and Daniil Selikhanovych and César A. Uribe and Bo Jiang 0007 and Haoyue Wang and Shuzhong Zhang and Sébastien Bubeck and Qijia Jiang and Yin Tat Lee and Yuanzhi Li and Aaron Sidford},
year = {2019},
url = {http://proceedings.mlr.press/v99/gasnikov19b.html},
researchr = {https://researchr.org/publication/GasnikovDGVSU0W19},
cites = {0},
citedby = {0},
pages = {1392-1393},
booktitle = {Conference on Learning Theory, COLT 2019, 25-28 June 2019, Phoenix, AZ, USA},
editor = {Alina Beygelzimer and Daniel Hsu 0001},
volume = {99},
series = {Proceedings of Machine Learning Research},
publisher = {PMLR},
}