ICML 2015poster205 citations
Strongly Adaptive Online Learning
Amit Daniely, Alon Gonen, Shai Shalev-Shwartz
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.
BibTeX
@InProceedings{pmlr-v37-daniely15,
title = {Strongly Adaptive Online Learning},
author = {Daniely, Amit and Gonen, Alon and Shalev-Shwartz, Shai},
booktitle = {Proceedings of the 32nd International Conference on Machine Learning},
pages = {1405--1411},
year = {2015},
editor = {Bach, Francis and Blei, David},
volume = {37},
series = {Proceedings of Machine Learning Research},
address = {Lille, France},
month = {07--09 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v37/daniely15.pdf},
url = {https://proceedings.mlr.press/v37/daniely15.html},
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.}
}