  We present AI-SARAH, a practical variant of SARAH. As a variant of SARAH,
this algorithm employs the stochastic recursive gradient yet adjusts step-size
based on local geometry. AI-SARAH implicitly computes step-size and efficiently
estimates local Lipschitz smoothness of stochastic functions. It is fully
adaptive, tune-free, straightforward to implement, and computationally
efficient. We provide technical insight and intuitive illustrations on its
design and convergence. We conduct extensive empirical analysis and demonstrate
its strong performance compared with its classical counterparts and other
state-of-the-art first-order methods in solving convex machine learning
problems.
