Improved Generalization Bounds for Adversarially Robust Learning

Understand

We consider a model of robust learning in an adversarial environment.

• The learner gets uncorrupted training data with access to possible corruptions that may be affected by the adversary during testing.
• The learner's goal is to build a robust classifier, which will be tested on future adversarial examples.
• The adversary is limited to $k$ possible corruptions for each input.