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Empirical bounds for functions with weak interactions

Published 11 Mar 2018 in stat.ML | (1803.03934v1)

Abstract: We provide sharp empirical estimates of expectation, variance and normal approximation for a class of statistics whose variation in any argument does not change too much when another argument is modified. Examples of such weak interactions are furnished by U- and V-statistics, Lipschitz L-statistics and various error functionals of L2-regularized algorithms and Gibbs algorithms.

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