Online Non-Additive Path Learning Under Full and Partial Information
Abstract
We study the problem of online path learning with non-additive gains, which is a central problem appearing in several applications, including ensemble structured prediction. We present new online algorithms for path learning with non-additive count-based gains for the three settings of full information, semi-bandit and full bandit with very favorable regret guarantees. A key component of our algorithms is the definition and computation of an intermediate context-dependent automaton that enables us to use existing algorithms designed for additive gains. We further apply our methods to the important application of ensemble structured prediction. Finally, beyond count-based gains, we give an efficient implementation of the EXP3 algorithm for the full bandit setting with an arbitrary (non-additive) gain.
Cite
Text
Cortes et al. "Online Non-Additive Path Learning Under Full and Partial Information." Proceedings of the 30th International Conference on Algorithmic Learning Theory, 2019.Markdown
[Cortes et al. "Online Non-Additive Path Learning Under Full and Partial Information." Proceedings of the 30th International Conference on Algorithmic Learning Theory, 2019.](https://mlanthology.org/alt/2019/cortes2019alt-online/)BibTeX
@inproceedings{cortes2019alt-online,
title = {{Online Non-Additive Path Learning Under Full and Partial Information}},
author = {Cortes, Corinna and Kuznetsov, Vitaly and Mohri, Mehryar and Rahmanian, Holakou and Warmuth, Manfred},
booktitle = {Proceedings of the 30th International Conference on Algorithmic Learning Theory},
year = {2019},
pages = {274-299},
volume = {98},
url = {https://mlanthology.org/alt/2019/cortes2019alt-online/}
}