Improved Strongly Adaptive Online Learning Using Coin Betting
Abstract
This paper describes a new parameter-free online learning algorithm for changing environments. In comparing against algorithms with the same time complexity as ours, we obtain a strongly adaptive regret bound that is a factor of at least $\sqrt{\log(T)}$ better, where $T$ is the time horizon. Empirical results show that our algorithm outperforms state-of-the-art methods in learning with expert advice and metric learning scenarios.
Cite
Text
Jun et al. "Improved Strongly Adaptive Online Learning Using Coin Betting." International Conference on Artificial Intelligence and Statistics, 2017.Markdown
[Jun et al. "Improved Strongly Adaptive Online Learning Using Coin Betting." International Conference on Artificial Intelligence and Statistics, 2017.](https://mlanthology.org/aistats/2017/jun2017aistats-improved/)BibTeX
@inproceedings{jun2017aistats-improved,
title = {{Improved Strongly Adaptive Online Learning Using Coin Betting}},
author = {Jun, Kwang-Sung and Orabona, Francesco and Wright, Stephen J. and Willett, Rebecca},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2017},
pages = {943-951},
url = {https://mlanthology.org/aistats/2017/jun2017aistats-improved/}
}