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ICML 2017poster27 citations

Uniform Deviation Bounds for k-Means Clustering

Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause

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Abstract

Uniform deviation bounds limit the difference between a model’s expected loss and its loss on an empirical sample

BibTeX
@InProceedings{pmlr-v70-bachem17a,
  title = 	 {Uniform Deviation Bounds for k-Means Clustering},
  author =       {Olivier Bachem and Mario Lucic and S. Hamed Hassani and Andreas Krause},
  booktitle = 	 {Proceedings of the 34th International Conference on Machine Learning},
  pages = 	 {283--291},
  year = 	 {2017},
  editor = 	 {Precup, Doina and Teh, Yee Whye},
  volume = 	 {70},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {06--11 Aug},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v70/bachem17a/bachem17a.pdf},
  url = 	 {https://proceedings.mlr.press/v70/bachem17a.html},
  abstract = 	 {Uniform deviation bounds limit the difference between a model’s expected loss and its loss on an empirical sample
Uniform Deviation Bounds for k-Means Clustering · ICML 2017