Rademacher Complexity for Adversarially Robust Generalization

29 Oct 2018Dong YinKannan RamchandranPeter Bartlett

Many machine learning models are vulnerable to adversarial attacks; for example, adding adversarial perturbations that are imperceptible to humans can often make machine learning models produce wrong predictions with high confidence. Moreover, although we may obtain robust models on the training dataset via adversarial training, in some problems the learned models cannot generalize well to the test data... (read more)

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