Robustness and Generalization

Huan Xu; Shie Mannor

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arXiv · 1005.2243

Robustness and Generalization

Huan Xu·Shie Mannor

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Abstract

We derive generalization bounds for learning algorithms based on their robustness: the property that if a testing sample is "similar" to a training sample, then the testing error is close to the training error. This provides a novel approach, different from the complexity or stability arguments, to study generalization of learning algorithms. We further show that a weak notion of robustness is both sufficient and necessary for generalizability, which implies that robustness is a fundamental property for learning algorithms to work.

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Huan Xu, Shie Mannor. 2010-05-13. Robustness and Generalization. https://arxiv.org/abs/1005.2243

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