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Dependent randomized rounding for cluste...
Harris, David G....

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Dependent randomized rounding for clustering and partition systems with knapsack constraints by Harris, David G. ( Author )
Lemonjar Open Access
Australian National University
10-08-2023
Clustering problems are fundamental to unsupervised learning. There is an increased emphasis on 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 clustering problems, can be formulated with "knapsack" and "partition" constraints. We develop new randomized algorithms targeting such problems, and study two in particular: multi-knapsack median and multi-knapsack center. Our rounding algorithms give new approximation and pseudo-approximation algorithms for these problems. One key technical tool, which may be of independent interest, is a new tail bound analogous to Feige (2006) for sums of random variables with unbounded variances. Such bounds can be useful in inferring properties of large networks using few samples.
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Article
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30.00 KB
English
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MYR 0.01
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http://arxiv.org/abs/1709.06995
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