On the hardness of robust classification
It is becoming increasingly important to understand the vulnerability of machine learning models to adversarial attacks. In this paper we study the feasibility of robust learning from the perspective of computational learning theory, considering both sample and computational complexity. In particula...
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
Neural Information Processing Systems Foundation
2019
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