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arXiv · 2006.14950

Relative Deviation Margin Bounds

Corinna Cortes·Mehryar Mohri·Ananda Theertha Suresh
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Abstract

We present a series of new and more favorable margin-based learning guarantees that depend on the empirical margin loss of a predictor. We give two types of learning bounds, both distribution-dependent and valid for general families, in terms of the Rademacher complexity or the empirical $\ell_\infty$ covering number of the hypothesis set used. Furthermore, using our relative deviation margin bounds, we derive distribution-dependent generalization bounds for unbounded loss functions under the assumption of a finite moment. We also briefly highlight several applications of these bounds and discuss their connection with existing results.

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Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh. 2020-10-28. Relative Deviation Margin Bounds. https://arxiv.org/abs/2006.14950

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