Online Learning for Neural Network Verification
This ongoing research explores the application of online learning techniques to improve neural network verification efficiency. By developing reinforcement learning methods that adapt branching strategies during verification, we aim to significantly reduce computational costs while maintaining formal guarantees. Our work extends the Marabou verification framework with learned decision heuristics that show promise for overcoming scalability challenges in neural network safety verification.
Dec 1, 2024
