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...
| Những tác giả chính: | , , |
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| Định dạng: | Conference item |
| Ngôn ngữ: | English |
| Được phát hành: |
IEEE
2024
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