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arxiv: 1803.03934 · v1 · pith:76LFUYITnew · submitted 2018-03-11 · 📊 stat.ML

Empirical bounds for functions with weak interactions

classification 📊 stat.ML
keywords algorithmsargumentempiricalinteractionsweakanotherapproximationbounds
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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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