Combinatorial Online Prediction via Metarounding
Abstract
We consider online prediction problems of combinatorial concepts. Examples of such concepts include s - t paths, permutations, truth assignments, set covers, and so on. The goal of the online prediction algorithm is to compete with the best fixed combinatorial concept in hindsight. A generic approach to this problem is to design an online prediction algorithm using the corresponding offline (approximation) algorithm as an oracle. The current state-of-the art method, however, is not efficient enough. In this paper we propose a more efficient online prediction algorithm when the offline approximation algorithm has a guarantee of the integrality gap.
Cite
Text
Fujita et al. "Combinatorial Online Prediction via Metarounding." International Conference on Algorithmic Learning Theory, 2013. doi:10.1007/978-3-642-40935-6_6Markdown
[Fujita et al. "Combinatorial Online Prediction via Metarounding." International Conference on Algorithmic Learning Theory, 2013.](https://mlanthology.org/alt/2013/fujita2013alt-combinatorial/) doi:10.1007/978-3-642-40935-6_6BibTeX
@inproceedings{fujita2013alt-combinatorial,
title = {{Combinatorial Online Prediction via Metarounding}},
author = {Fujita, Takahiro and Hatano, Kohei and Takimoto, Eiji},
booktitle = {International Conference on Algorithmic Learning Theory},
year = {2013},
pages = {68-82},
doi = {10.1007/978-3-642-40935-6_6},
url = {https://mlanthology.org/alt/2013/fujita2013alt-combinatorial/}
}