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Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates
In this work, we show that this problem also affects robustness to adversarial examples, thereby hindering the development of secure model update practices.
The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers
We derive here new generalization bounds, based on Rademacher Complexity theory, for model selection and error estimation of linear (kernel) classifiers, which exploit the availability of unlabeled samples.
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