NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:6867
Title:Continuous-time Models for Stochastic Optimization Algorithms

The paper presents an SDE approximation of mini-batch stochastic gradient descent and stochastic variance reduction gradient descent, two widely used methods, and they derive convergence rates. Well written paper. It presents a nice (i.e., not revolutionary, but still of interest to the community) result that fits within this area. Reviewers have a few suggestions for clarifications/improvements. [This meta-review was reviewed and revised by the Program Chairs]