Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates

NeurIPS 2019 Sharan VaswaniAaron MishkinIssam LaradjiMark SchmidtGauthier GidelSimon Lacoste-Julien

Recent works have shown that stochastic gradient descent (SGD) achieves the fast convergence rates of full-batch gradient descent for over-parameterized models satisfying certain interpolation conditions. However, the step-size used in these works depends on unknown quantities and SGD's practical performance heavily relies on the choice of this step-size... (read more)

PDF Abstract

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.