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Improved generalization bounds for robust learning.

algorithmic learning theory, (2019)

Cited by: 25|Views89
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Abstract

consider a model of robust learning in an adversarial environment. The learner gets uncorrupted training data with access to possible corruptions that may be used by the adversary during testing. Their aim is to build a robust classifier that would be tested on future adversarially corrupted data. use a zero-sum game between the learner...More

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