On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics

JOURNAL OF MACHINE LEARNING RESEARCH, pp. 1-41, 2020.

Cited by: 2|Views105
EI

Abstract:

Stochastic gradient Langevin dynamics (SGLD) is a fundamental algorithm in stochastic optimization. Recent work by Zhang et al. (2017) presents an analysis for the hitting time of SGLD for the first and second order stationary points. The proof in Zhang et al. (2017) is a two-stage procedure through bounding the Cheeger's constant, which ...More

Code:

Data:

Get fulltext within 24h
Bibtex
Your rating :
0

 

Tags
Comments