Online Algorithms for Rent-or-Buy with Expert Advice
Abstract
We study the use of predictions by multiple experts (such as machine learning algorithms) to improve the performance of online algorithms. In particular, we consider the classical rent-or-buy problem (also called ski rental), and obtain algorithms that provably improve their performance over the adversarial scenario by using these predictions. We also prove matching lower bounds to show that our algorithms are the best possible, and perform experiments to empirically validate their performance in practice
Cite
Text
Gollapudi and Panigrahi. "Online Algorithms for Rent-or-Buy with Expert Advice." International Conference on Machine Learning, 2019.Markdown
[Gollapudi and Panigrahi. "Online Algorithms for Rent-or-Buy with Expert Advice." International Conference on Machine Learning, 2019.](https://mlanthology.org/icml/2019/gollapudi2019icml-online/)BibTeX
@inproceedings{gollapudi2019icml-online,
title = {{Online Algorithms for Rent-or-Buy with Expert Advice}},
author = {Gollapudi, Sreenivas and Panigrahi, Debmalya},
booktitle = {International Conference on Machine Learning},
year = {2019},
pages = {2319-2327},
volume = {97},
url = {https://mlanthology.org/icml/2019/gollapudi2019icml-online/}
}