AI RESEARCH
Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planes
arXiv CS.LG
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ArXi:2501.00200v2 Announce Type: replace Recently, cutting-plane methods such as GCP-CROWN have been explored to enhance neural network verifiers and made significant advances. However, GCP-CROWN currently relies on generic cutting planes (cuts) generated from external mixed integer programming (MIP) solvers. Due to the poor scalability of MIP solvers, large neural networks cannot benefit from these cutting planes. In this paper, we exploit the structure of the neural network verification problem to generate efficient and scalable cutting planes specific for this problem setting.