activity
20202022
most citedFast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers

60 citations · 69 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG20228 cited

Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation

Zhouxing Shi, Yihan Wang, Huan Zhang +2

Lipschitz constants are connected to many properties of neural networks, such as robustness, fairness, and generalization. Existing methods for computing Lipschitz constants either…

cs.LG2022

On the Convergence of Certified Robust Training with Interval Bound Propagation

Yihan Wang, Zhouxing Shi, Quanquan Gu +1

Interval Bound Propagation (IBP) is so far the base of state-of-the-art methods for training neural networks with certifiable robustness guarantees when potential adversarial pertu…

cs.LG2021

Fast Certified Robust Training with Short Warmup

Zhouxing Shi, Yihan Wang, Huan Zhang +2

Recently, bound propagation based certified robust training methods have been proposed for training neural networks with certifiable robustness guarantees. Despite that state-of-th…

cs.AI202060 cited

Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers

Kaidi Xu, Huan Zhang, Shiqi Wang +4

Formal verification of neural networks (NNs) is a challenging and important problem. Existing efficient complete solvers typically require the branch-and-bound (BaB) process, which…

cs.LG20201 cited

On -norm Robustness of Ensemble Stumps and Trees

Yihan Wang, Huan Zhang, Hongge Chen +2

Recent papers have demonstrated that ensemble stumps and trees could be vulnerable to small input perturbations, so robustness verification and defense for those models have become…