Parallel Online Learning

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.

• Type: Journal
• Year: 2011
• Where: CORR
• Authors: Daniel Hsu, Nikos Karampatziakis, John Langford, Alexander J. Smola
• Keywords: CORR 2011, Education, Impressive Convergence Properties, Modern Multi-core Architectures, Online