Uniform concentration and symmetrization for weak interactions

Maurer, Andreas, Pontil, Massimiliano

arXiv.org Machine Learning 

The method to derive uniform bounds with Gaussian and Rademacher complexities is extended to the case where the sample average is replaced by a nonlinear statistic. Tight bounds are obtained for U-statistics, smoothened L-statistics and error functionals of l2-regularized algorithms.

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