Abstract
Concentration inequalities deal with deviations of functions of independent random variables from their expectation. In the last decade new tools have been introduced making it possible to establish simple and powerful inequalities. These inequalities are at the heart of the mathematical analysis of various problems in machine learning and made it possible to derive new efficient algorithms. This text attempts to summarize some of the basic tools.
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CITATION STYLE
APA
Concentration Inequalities. (2007). In Concentration Inequalities and Model Selection (pp. 147–181). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-48503-2_5
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