2026
Improving Adversarial Robustness of Attribution via Implicit Regularization
ICML 2026poster
The adversarial robustness of attributions is a fundamental requirement for reliable explainability in deep learning, yet existing approaches typically rely on computationally expensive explicit regularization. In this work, we show that attribution robustness can arise implicitly from the learning …