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David Harris

2 accepted papers

2020

Dependent randomized rounding for clustering and partition systems with knapsack constraints

AISTATS 2020poster

Clustering problems are fundamental to unsupervised learning. There is an increased emphasis on \emph{fairness} in machine learning and AI; one representative notion of fairness is that no single demographic group should be over-represented among the cluster-centers. This, and much more general clus…

Cited by 3SourcePDFScholar
2018

Approximation algorithms for stochastic clustering

NeurIPS 2018poster

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…

Cited by 16SourcePDFScholar