2 citations · 3 across the 5 of their papers we have counts for
5 papers
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…
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)…
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…
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…
Improving the Generation Quality of Watermarked Large Language Models via Word Importance Scoring
Yuhang Li, Yihan Wang, Zhouxing Shi +1
The strong general capabilities of Large Language Models (LLMs) bring potential ethical risks if they are unrestrictedly accessible to malicious users. Token-level watermarking ins…