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An accessible account of the rich theory surrounding concentration inequalities in probability theory, with applications from machine learning and statistics to high-dimensional geometry. This book introduces key ideas and presents a detailed summary of the state-of-the-art in the area making it ideal for independent learning and as a reference
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Boucheron et al. (2013) studied this question.