WOR and $p
#x27;s: Sketches for $\ell_p$-Sampling Without Replacement

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Abstract:

Weighted sampling is a fundamental tool in data analysis and machine learning pipelines. Samples are used for efficient estimation of statistics or as sparse representations of the data. When weight distributions are skewed, as is often the case in practice, without-replacement (WOR) sampling is much more effective than with-replacement...More

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