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Published on Nov 18, 2016
Presented by Edith Cohen, Google // Visiting Professor at the School of Computer Science at Tel Aviv University in Israel
Abstract: Random sampling is a classic tool for surveying properties and statistics of populations: Samples capture the essence of the data so that properties of the data can be approximated by estimators applied to the sample. Sampling schemes are tailored to the tasks at hand, and seeks to balance size, approximation quality, and computation.
Historically, sampling is as old as human learning. Some landmarks are its use by Laplace (1802) to estimate the population of France, and first use by the US census (1938) to estimate unemployment rate. With the emergence of massive data sets, sampling became an essential tool for scaling up computation (numerical optimization, clustering, submodular maximization) and leveraging data such as traffic or activity logs that are too large to process or store longer term.
In this talk, Dr. Cohen will highlight some favourite selected applications and sampling schemes. In particular, samples as locality-sensitive hashes, multi-objective samples, and sampling of streamed or distributed data.