Online Facility Location with Multiple Advice
Abstract
Clustering is a central topic in unsupervised learning and its online formulation has received a lot of attention in recent years. In this paper, we study the classic facility location problem in the presence of multiple machine-learned advice. We design an algorithm with provable performance guarantees such that, if the advice is good, it outperforms the best-known online algorithms for the problem, and if it is bad it still matches their performance.We complement our theoretical analysis with an in-depth study of the performance of our algorithm, showing its effectiveness on synthetic and real-world data sets.
Cite
Text
Almanza et al. "Online Facility Location with Multiple Advice." Neural Information Processing Systems, 2021.Markdown
[Almanza et al. "Online Facility Location with Multiple Advice." Neural Information Processing Systems, 2021.](https://mlanthology.org/neurips/2021/almanza2021neurips-online/)BibTeX
@inproceedings{almanza2021neurips-online,
title = {{Online Facility Location with Multiple Advice}},
author = {Almanza, Matteo and Chierichetti, Flavio and Lattanzi, Silvio and Panconesi, Alessandro and Re, Giuseppe},
booktitle = {Neural Information Processing Systems},
year = {2021},
url = {https://mlanthology.org/neurips/2021/almanza2021neurips-online/}
}