Strongly Adaptive Online Learning

Amit Daniely · Alon Gonen · Shai Shalev‐Shwartz

77 citations0/0 files storedarXiv (Cornell University)

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Advanced Bandit Algorithms Research Machine Learning and Algorithms Optimization and Search Problems

Abstract

Strongly adaptive algorithms are algorithms whose performance on every time interval is close to optimal. We present a reduction that can transform standard low-regret algorithms to strongly adaptive. As a consequence, we derive simple, yet efficient, strongly adaptive algorithms for a handful of problems.

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2002The Nonstochastic Multiarmed Bandit Problem 2011Online Learning and Online Convex Optimization 1997How to use expert advice 1998Tracking the Best Expert 1997Using and combining predictors that specialize 2015Online Optimization : Competing with Dynamic Comparators 2018From External to Internal Regret 2001Tracking a Small Set of Experts by Mixing Past Posteriors

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2020Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles 2015Learning and Efficiency in Games with Dynamic Population 2017Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics 2021Learning and Information in Stochastic Networks and Queues

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