A Stochastic Operator Framework for Optimization and Learning with Sub-Weibull Errors

Understand

This paper proposes a framework to study the convergence of stochastic optimization and learning algorithms.

• The framework is modeled over the different challenges that these algorithms pose, such as (i) the presence of random additive errors (e.g.
• due to stochastic gradients), and (ii) random coordinate updates (e.g.
• due to asynchrony in distributed set-ups).