ICML 2015Poster

Strongly Adaptive Online Learning

作者: Amit Daniely, Alon Gonen, Shai Shalev-Shwartz

摘要

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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