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Learning without concentration for general loss functions

Mendelson, Shahar


We study the performance of empirical risk minimization in prediction and estimation problems that are carried out in a convex class and relative to a sufficiently smooth convex loss function. The framework is based on the small-ball method and thus is suited for heavy-tailed problems. Moreover, among its outcomes is that a well-chosen loss, calibrated to fit the noise level of the problem, negates some of the ill-effects of outliers and boosts the confidence level—leading to a gaussian like...[Show more]

CollectionsANU Research Publications
Date published: 2018
Type: Journal article
Source: Probability Theory and Related Fields
DOI: 10.1007/s00440-017-0784-y


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