3 papers
cs.LG2026
Certified Training with Branch-and-Bound for Lyapunov-stable Neural Control
Zhouxing Shi, Haoyu Li, Cho-Jui Hsieh +1
We study the problem of learning verifiably Lyapunov-stable neural controllers that provably satisfy the Lyapunov asymptotic stability condition within a region-of-attraction (ROA)…
cs.LG2025
SoundnessBench: A Soundness Benchmark for Neural Network Verifiers
Xingjian Zhou, Keyi Shen, Andy Xu +4
Neural network (NN) verification aims to formally verify properties of NNs, which is crucial for ensuring the behavior of NN-based models in safety-critical applications. In recent…
cs.LG2025
Neural Network Verification with Branch-and-Bound for General Nonlinearities
Zhouxing Shi, Qirui Jin, Zico Kolter +3
Branch-and-bound (BaB) is among the most effective techniques for neural network (NN) verification. However, existing works on BaB for NN verification have mostly focused on NNs wi…