no code implementations • ICML 2020 • Yihan Wang, huan zhang, Hongge Chen, Duane Boning, Cho-Jui Hsieh
In this paper, we study the robustness verification and defense with respect to general $\ell_p$ norm perturbation for ensemble trees and stumps.
no code implementations • 18 Oct 2022 • Eli Bronstein, Mark Palatucci, Dominik Notz, Brandyn White, Alex Kuefler, Yiren Lu, Supratik Paul, Payam Nikdel, Paul Mougin, Hongge Chen, Justin Fu, Austin Abrams, Punit Shah, Evan Racah, Benjamin Frenkel, Shimon Whiteson, Dragomir Anguelov
We demonstrate the first large-scale application of model-based generative adversarial imitation learning (MGAIL) to the task of dense urban self-driving.
2 code implementations • ICLR 2021 • huan zhang, Hongge Chen, Duane Boning, Cho-Jui Hsieh
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.
1 code implementation • 20 Aug 2020 • Yihan Wang, huan zhang, Hongge Chen, Duane Boning, Cho-Jui Hsieh
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.
no code implementations • NeurIPS 2020 • Hongge Chen, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar, Duane Boning, Cho-Jui Hsieh
With this score, we can identify the pretraining examples in the pretraining task that contribute most to a prediction in the finetuning task.
4 code implementations • NeurIPS 2020 • Huan Zhang, Hongge Chen, Chaowei Xiao, Bo Li, Mingyan Liu, Duane Boning, Cho-Jui Hsieh
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.
1 code implementation • ECCV 2020 • Kaidi Xu, Gaoyuan Zhang, Sijia Liu, Quanfu Fan, Mengshu Sun, Hongge Chen, Pin-Yu Chen, Yanzhi Wang, Xue Lin
To the best of our knowledge, this is the first work that models the effect of deformation for designing physical adversarial examples with respect to-rigid objects such as T-shirts.
2 code implementations • ICLR 2020 • Huan Zhang, Hongge Chen, Chaowei Xiao, Sven Gowal, Robert Stanforth, Bo Li, Duane Boning, Cho-Jui Hsieh
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.
1 code implementation • 10 Jun 2019 • Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, Xue Lin
Graph neural networks (GNNs) which apply the deep neural networks to graph data have achieved significant performance for the task of semi-supervised node classification.
2 code implementations • NeurIPS 2019 • Hongge Chen, huan zhang, Si Si, Yang Li, Duane Boning, Cho-Jui Hsieh
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.
3 code implementations • 27 Feb 2019 • Hongge Chen, huan zhang, Duane Boning, Cho-Jui Hsieh
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.
no code implementations • ICLR 2019 • Huan Zhang, Hongge Chen, Zhao Song, Duane Boning, Inderjit S. Dhillon, Cho-Jui Hsieh
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.
2 code implementations • ECCV 2018 • Dong Su, huan zhang, Hongge Chen, Jin-Feng Yi, Pin-Yu Chen, Yupeng Gao
The prediction accuracy has been the long-lasting and sole standard for comparing the performance of different image classification models, including the ImageNet competition.
6 code implementations • ICML 2018 • Tsui-Wei Weng, huan zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Duane Boning, Inderjit S. Dhillon, Luca Daniel
Verifying the robustness property of a general Rectified Linear Unit (ReLU) network is an NP-complete problem [Katz, Barrett, Dill, Julian and Kochenderfer CAV17].
2 code implementations • ACL 2018 • Hongge Chen, huan zhang, Pin-Yu Chen, Jin-Feng Yi, Cho-Jui Hsieh
Our extensive experiments show that our algorithm can successfully craft visually-similar adversarial examples with randomly targeted captions or keywords, and the adversarial examples can be made highly transferable to other image captioning systems.