Constructs k-inductive neural barrier certificates for partially unknown nonlinear dynamics by combining neural networks, a data-driven fundamental lemma from one trajectory, and CEGIS-SMT verification.
arXiv preprint arXiv:2502.05510 , year=
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A barrier-certificate framework certifies non-trivial robustness radii for neural networks under worst-case l_p poisoning during training and at test time, with PAC bounds derived via scenario convex programming.
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k-Inductive Neural Barrier Certificates for Unknown Nonlinear Dynamics
Constructs k-inductive neural barrier certificates for partially unknown nonlinear dynamics by combining neural networks, a data-driven fundamental lemma from one trajectory, and CEGIS-SMT verification.
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Robustness Certificates for Neural Networks against Adversarial Attacks
A barrier-certificate framework certifies non-trivial robustness radii for neural networks under worst-case l_p poisoning during training and at test time, with PAC bounds derived via scenario convex programming.