Skip to main content

Online Learning for Adversaries with Memory: Price of Past Mistakes

Author(s): Anava, Oren; Hazan, Elad; Mannor, Shie

To refer to this page use:
Abstract: The framework of online learning with memory naturally captures learning problems with temporal effects, and was previously studied for the experts setting. In this work we extend the notion of learning with memory to the general Online Convex Optimization (OCO) framework, and present two algorithms that attain low regret. The first algorithm applies to Lipschitz continuous loss functions, obtaining optimal regret bounds for both convex and strongly convex losses. The second algorithm attains the optimal regret bounds and applies more broadly to convex losses without requiring Lipschitz continuity, yet is more complicated to implement. We complement the theoretic results with two applications: statistical arbitrage in finance, and multi-step ahead prediction in statistics.
Publication Date: 2015
Citation: Anava, Oren, Elad Hazan, and Shie Mannor. "Online Learning for Adversaries with Memory: Price of Past Mistakes." In Advances in Neural Information Processing Systems 28 (2015).
Type of Material: Conference Article
Journal/Proceeding Title: Advances in Neural Information Processing Systems
Version: Final published version. Article is made available in OAR by the publisher's permission or policy.

Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.