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.
Online Facility Location with Multiple Advice / Almanza, Matteo; Chierichetti, Flavio; Lattanzi, Silvio; Panconesi, Alessandro; Re, Giuseppe. - 34:(2021), pp. 4661-4673. (Intervento presentato al convegno Neurips 2021: Advances in Neural Information Processing Systems 34 tenutosi a Virtual Event).
Online Facility Location with Multiple Advice
Matteo Almanza;Flavio Chierichetti;Alessandro Panconesi;Giuseppe Re
2021
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
