Online Facility Location with Multiple AdviceDownload PDF

21 May 2021, 20:50 (edited 21 Jan 2022)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: Clustering, Facility Location, Online Algorithms, Machine-Learned Advice, Online Clustering
  • 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.
  • Supplementary Material: pdf
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  • Code: https://github.com/matteojug/Online-Facility-Location-with-Multiple-Advice
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