Open Problem: Model Selection for Contextual Bandits
Abstract
In statistical learning, algorithms for model selection allow the learner to adapt to the complexity of the best hypothesis class in a sequence. We ask whether similar guarantees are possible for contextual bandit learning.
Cite
Text
Foster et al. "Open Problem: Model Selection for Contextual Bandits." Conference on Learning Theory, 2020.Markdown
[Foster et al. "Open Problem: Model Selection for Contextual Bandits." Conference on Learning Theory, 2020.](https://mlanthology.org/colt/2020/foster2020colt-open/)BibTeX
@inproceedings{foster2020colt-open,
title = {{Open Problem: Model Selection for Contextual Bandits}},
author = {Foster, Dylan J. and Krishnamurthy, Akshay and Luo, Haipeng},
booktitle = {Conference on Learning Theory},
year = {2020},
pages = {3842-3846},
volume = {125},
url = {https://mlanthology.org/colt/2020/foster2020colt-open/}
}