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.
In: Proceed- ings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019
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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.