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Online Learning for Adversaries with Memory: Price of Past Mistakes
Oren Anava · Elad Hazan · Shie Mannor

Thu Dec 10 08:00 AM -- 12:00 PM (PST) @ 210 C #90

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.

Author Information

Oren Anava (Technion)
Elad Hazan (Princeton University)
Shie Mannor (Technion)

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