60 citations · 106 across the 5 of their papers we have counts for
7 papers
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…
Multi-Stage Influence Function
Hongge Chen, Si Si, Yang Li +4
Multi-stage training and knowledge transfer, from a large-scale pretraining task to various finetuning tasks, have revolutionized natural language processing and computer vision re…
Towards Stable and Efficient Training of Verifiably Robust Neural Networks
Huan Zhang, Hongge Chen, Chaowei Xiao +5
Training neural networks with verifiable robustness guarantees is challenging. Several existing approaches utilize linear relaxation based neural network output bounds under pertur…
Robustness Verification of Tree-based Models
Hongge Chen, Huan Zhang, Si Si +3
We study the robustness verification problem for tree-based models, including decision trees, random forests (RFs) and gradient boosted decision trees (GBDTs). Formal robustness ve…
Robust Decision Trees Against Adversarial Examples
Hongge Chen, Huan Zhang, Duane Boning +1
Although adversarial examples and model robustness have been extensively studied in the context of linear models and neural networks, research on this issue in tree-based models an…
The Limitations of Adversarial Training and the Blind-Spot Attack
Huan Zhang, Hongge Chen, Zhao Song +3
The adversarial training procedure proposed by Madry et al. (2018) is one of the most effective methods to defend against adversarial examples in deep neural networks (DNNs). In ou…