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20242026
most citedNeural Network Verification with Branch-and-Bound for General Nonlinearities

2 citations · 4 across the 12 of their papers we have counts for

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7 papers · 1 filter

cs.LG2024

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.LG2024

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.LG2024

AutoSpec: Automated Generation of Neural Network Specifications

Shuowei Jin, Taobo Liao, Anuj Kalia +5

The increasing adoption of neural networks in learning-augmented systems highlights the growing need for model safety and robustness, especially in safety-critical domains. While r…

cs.LG2024

PREMAP: A Unifying PREiMage APproximation Framework for Neural Networks

Xiyue Zhang, Benjie Wang, Marta Kwiatkowska +1

Most methods for neural network verification focus on bounding the image, i.e., set of outputs for a given input set. This can be used to, for example, check the robustness of neur…

cs.LG2024★ 2 cited

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…

cs.LG2024★ 1 cited

Verified Safe Reinforcement Learning for Neural Network Dynamic Models

Junlin Wu, Huan Zhang, Yevgeniy Vorobeychik

Learning reliably safe autonomous control is one of the core problems in trustworthy autonomy. However, training a controller that can be formally verified to be safe remains a maj…

cs.LG2024★ 1 cited

Lyapunov-stable Neural Control for State and Output Feedback: A Novel Formulation

Lujie Yang, Hongkai Dai, Zhouxing Shi +3

Learning-based neural network (NN) control policies have shown impressive empirical performance in a wide range of tasks in robotics and control. However, formal (Lyapunov) stabili…