60 citations · 145 across the 12 of their papers we have counts for
21 papers
Are AlphaZero-like Agents Robust to Adversarial Perturbations?
Li-Cheng Lan, Huan Zhang, Ti-Rong Wu +3
The success of AlphaZero (AZ) has demonstrated that neural-network-based Go AIs can surpass human performance by a large margin. Given that the state space of Go is extremely large…
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…
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks
Fan Wu, Linyi Li, Chejian Xu +5
As reinforcement learning (RL) has achieved near human-level performance in a variety of tasks, its robustness has raised great attention. While a vast body of research has explore…
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds
Yujia Huang, Huan Zhang, Yuanyuan Shi +2
Certified robustness is a desirable property for deep neural networks in safety-critical applications, and popular training algorithms can certify robustness of a neural network by…
Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training
Alexander Pan, Yongkyun Lee, Huan Zhang +2
Due to the proliferation of renewable energy and its intrinsic intermittency and stochasticity, current power systems face severe operational challenges. Data-driven decision-makin…
Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation
Chong Zhang, Jieyu Zhao, Huan Zhang +2
Robustness and counterfactual bias are usually evaluated on a test dataset. However, are these evaluations robust? If the test dataset is perturbed slightly, will the evaluation re…