The Primal-Dual method for Learning Augmented Algorithms
Machine Learning
2020-10-23 v1 Data Structures and Algorithms
Abstract
The extension of classical online algorithms when provided with predictions is a new and active research area. In this paper, we extend the primal-dual method for online algorithms in order to incorporate predictions that advise the online algorithm about the next action to take. We use this framework to obtain novel algorithms for a variety of online covering problems. We compare our algorithms to the cost of the true and predicted offline optimal solutions and show that these algorithms outperform any online algorithm when the prediction is accurate while maintaining good guarantees when the prediction is misleading.
Cite
@article{arxiv.2010.11632,
title = {The Primal-Dual method for Learning Augmented Algorithms},
author = {Étienne Bamas and Andreas Maggiori and Ola Svensson},
journal= {arXiv preprint arXiv:2010.11632},
year = {2020}
}
Comments
30 pages, 11 figures. To appear in NeurIPS 2020 (oral)