  Online learning algorithms have impressive convergence properties when it
comes to risk minimization and convex games on very large problems. However,
they are inherently sequential in their design which prevents them from taking
advantage of modern multi-core architectures. In this paper we prove that
online learning with delayed updates converges well, thereby facilitating
parallel online learning.

Optimization and Control;Machine Learning