Robust optimization for adversarial learning with finite sample complexity guarantees
Decision making and learning in the presence of uncertainty has attracted significant attention in view of the increasing need to achieve robust and reliable operations. In the case where uncertainty stems from the presence of adversarial attacks this need is becoming more prominent. In this paper w...
| Huvudupphovsmän: | , , |
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| Materialtyp: | Conference item |
| Språk: | English |
| Publicerad: |
IEEE
2024
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