A regression-tree-based method computes guaranteed bounds on the safe output probability for neural networks under probabilistic inputs by generating safe and unsafe hulls via boundary-aware sampling and prioritized refinement.
(eds.) Proceedings of the 20th International Symposium on Automated Technology for Verification and Analysis
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Probabilistic Verification of Neural Networks via Efficient Probabilistic Hull Generation
A regression-tree-based method computes guaranteed bounds on the safe output probability for neural networks under probabilistic inputs by generating safe and unsafe hulls via boundary-aware sampling and prioritized refinement.