CT-BaB integrates branch-and-bound during training to tighten certified Lyapunov bounds, yielding neural controllers with 164X larger verifiable ROA and 11X faster verification than CEGIS on a 2D quadrotor.
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CURE is the first multi-norm certified training method that improves union robustness across l_p norms and unseen perturbations on MNIST, CIFAR-10 and TinyImagenet.
Proposes spectral norm of Fisher Information Matrix as attack-agnostic robustness metric with closed-form bounds for common architectures and correlation to adversarial vulnerability.
Certified robustness varies extremely across training seeds with std larger than recent gains, and generalizes poorly to unseen data.
citing papers explorer
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Certified Training with Branch-and-Bound for Lyapunov-stable Neural Control
CT-BaB integrates branch-and-bound during training to tighten certified Lyapunov bounds, yielding neural controllers with 164X larger verifiable ROA and 11X faster verification than CEGIS on a 2D quadrotor.
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Towards Generalized Certified Robustness with Multi-Norm Training
CURE is the first multi-norm certified training method that improves union robustness across l_p norms and unseen perturbations on MNIST, CIFAR-10 and TinyImagenet.
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Measuring Model Robustness via Fisher Information: Spectral Bounds, Theoretical Guarantees, and Practical Algorithms
Proposes spectral norm of Fisher Information Matrix as attack-agnostic robustness metric with closed-form bounds for common architectures and correlation to adversarial vulnerability.
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On the Extreme Variance of Certified Local Robustness Across Model Seeds
Certified robustness varies extremely across training seeds with std larger than recent gains, and generalizes poorly to unseen data.