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Sketching methods

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If you have a question about this talk, please contact Yingzhen Li.

Sketching methods (or sometimes called streaming algorithms) are useful when you want an approximate property of a dataset when the computation of its true value would take too long or use too much memory. In this talk we give a brief overview of some of the most popular sketching methods, including: the Flajolet-Martin algorithm, for estimating the cardinality of a dataset; the Bloom Filter for testing set membership and the Count-Min Sketch for estimating occurrences of each item. We will try to provide examples of when these methods are useful and where they are used in big data applications.

There is no need to read any material prior to the meeting.

This talk is part of the Machine Learning Reading Group @ CUED series.

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