NeurIPS 2018poster16 citations
Approximation algorithms for stochastic clustering
David Harris, Shi Li, Aravind Srinivasan, Khoa Trinh, Thomas Pensyl
Abstract
We consider stochastic settings for clustering, and develop provably-good (approximation) algorithms for a number of these notions. These algorithms allow one to obtain better approximation ratios compared to the usual deterministic clustering setting. Additionally, they offer a number of advantages including providing fairer clustering and clustering which has better long-term behavior for each user. In particular, they ensure that
BibTeX
@inproceedings{NEURIPS2018_3e60e09c,
author = {Harris, David and Li, Shi and Srinivasan, Aravind and Trinh, Khoa and Pensyl, Thomas},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Approximation algorithms for stochastic clustering},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/3e60e09c222f206c725385f53d7e567c-Paper.pdf},
volume = {31},
year = {2018}
}