Work

Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives

Year: 2019

Type: conference-paper

Source: Conference on Learning Theory

Authors: Alexander Gasnikov, Pavel Dvurechensky, Eduard Gorbunov, Evgeniya Vorontsova, Daniil Selikhanovych, César A. Uribe, Bo Jiang, Haoyue Wang, Shuzhong Zhang, Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford

Institutions: National Research University Higher School of Economics, Russian Academy of Sciences, Moscow Institute of Physics and Technology, Université Grenoble Alpes, Massachusetts Institute of Technology, Shanghai University of Finance and Economics, University of Minnesota, Microsoft (United States), Stanford University, University of Washington, Carnegie Mellon University

Language: English

Topic: Stochastic Gradient Optimization Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: closed

OpenAlex ID: W2965963244

FWCI: 1.722

Cites:

Cited by: 22

Related to: 10