LGFeb 25, 2015
Strongly Adaptive Online Learning
arXiv:1502.07073v326.5193 citations
Originality Incremental advance
AI Analysis
This work addresses the need for more robust online learning algorithms in machine learning, though it appears incremental as it builds on existing low-regret methods.
The paper tackles the problem of making online learning algorithms perform near-optimally on every time interval by introducing a reduction that transforms standard low-regret algorithms into strongly adaptive ones, resulting in efficient algorithms for various problems.
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.