Search Results for author:
Found 10 papers, 7 papers with code
On Lp-norm Robustness of Ensemble Decision Stumps and Trees
In this paper, we study the robustness verification and defense with respect to general $\ell_p$ norm perturbation for ensemble trees and stumps.
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary
We study the robustness of reinforcement learning (RL) with adversarially perturbed state observations, which aligns with the setting of many adversarial attacks to deep reinforcement learning (DRL) and is also important for rolling out real-world RL agent under unpredictable sensing noise.
On $\ell_p$-norm Robustness of Ensemble Stumps and Trees
In this paper, we study the problem of robustness verification and certified defense with respect to general $\ell_p$ norm perturbations for ensemble decision stumps and trees.
Multi-Stage Influence Function
With this score, we can identify the pretraining examples in the pretraining task that contribute most to a prediction in the finetuning task.
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations
Several works have shown this vulnerability via adversarial attacks, but existing approaches on improving the robustness of DRL under this setting have limited success and lack for theoretical principles.
Towards Stable and Efficient Training of Verifiably Robust Neural Networks
In this paper, we propose a new certified adversarial training method, CROWN-IBP, by combining the fast IBP bounds in a forward bounding pass and a tight linear relaxation based bound, CROWN, in a backward bounding pass.
Robustness Verification of Tree-based Models
We show that there is a simple linear time algorithm for verifying a single tree, and for tree ensembles, the verification problem can be cast as a max-clique problem on a multi-partite graph with bounded boxicity.
Robust Decision Trees Against Adversarial Examples
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 and how to make tree-based models robust against adversarial examples is still limited.
The Limitations of Adversarial Training and the Blind-Spot Attack
In our paper, we shed some lights on the practicality and the hardness of adversarial training by showing that the effectiveness (robustness on test set) of adversarial training has a strong correlation with the distance between a test point and the manifold of training data embedded by the network.
Towards Fast Computation of Certified Robustness for ReLU Networks
Verifying the robustness property of a general Rectified Linear Unit (ReLU) network is an NP-complete problem [Katz, Barrett, Dill, Julian and Kochenderfer CAV17].
