Convergence Analysis of SGD under Expected Smoothness

Yuta Kawamoto, Hideaki Iiduka

Stochastic gradient descent (SGD) is the workhorse of large-scale learning, yet classical analyses rely on assumptions that can be either too strong (bounded variance) or too coars…