Yisen Wang

                      @inproceedings{wei2023cfa,
                        title={CFA: Class-wise Calibrated Fair Adversarial Training},
                        author={Wei, Zeming and Wang, Yifei and Guo, Yiwen and Wang, Yisen},
                        booktitle={CVPR},
                        year={2023}}
                

                      @inproceedings{kou2022certified,
                        title={Certified Adversarial Robustness Under the Bounded Support Set},
                        author={Kou, Yiwen and Zheng, Qinyuan and Wang, Yisen},
                        booktitle={ICML},
                        year={2022}}
                

                      @inproceedings{li2022cerdeq,
                        title={CerDEQ: Certifiable Deep Equilibrium Model},
                        author={Li, Mingjie and Wang, Yisen and Lin, Zhouchen},
                        booktitle={ICML},
                        year={2022}}
                

                      @inproceedings{wang2022self,
                        title={Self-Ensemble Adversarial Training for Improved Robustness},
                        author={Wang, Hongjun and Wang, Yisen},
                        booktitle={ICLR},
                        year={2022}}
                

                      @article{bai2023query,
                        title={Query efficient black-box adversarial attack on deep neural networks},
                        author={Bai, Yang and Wang, Yisen and Zeng, Yuyuan and Jiang, Yong and Xia, Shu-Tao},
                        journal={Pattern Recognition},
                        volume={133},
                        pages={109037},
                        year={2023},
                        publisher={Elsevier}}
                

                      @inproceedings{bai2021clustering,
                        title={Clustering Effect of (Linearized) Adversarial Robust Models},
                        author={Bai, Yang and Yan, Xin and Jiang, Yong and Xia, Shu-Tao and Wang, Yisen},
                        booktitle={NeurIPS},
                        year={2021}}
                

                      @inproceedings{niu2021morie,
                        title={Mori{\'e} Attack (MA): A New Potential Risk of Screen Photos},
                        author={Niu, Dantong and Guo, Ruohao and Wang, Yisen},
                        booktitle={NeurIPS},
                        year={2021}}
                

                      @inproceedings{ren2021unified,
                        title={A unified game-theoretic interpretation of adversarial robustness},
                        author={Ren, Jie and Zhang, Die and Wang, Yisen and Chen, Lu and Zhou, Zhanpeng and Chen, Yiting and Cheng, Xu and Wang, Xin and Zhou, Meng and Shi, Jie and others},
                        booktitle={NeurIPS},
                        year={2021}}
                

                    @inproceedings{huang2021exploring,
                      title={Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks},
                      author={Huang, Hanxun and Wang, Yisen and Erfani, Sarah Monazam and Gu, Quanquan and Bailey, James and Ma, Xingjun},
                      booktitle={NeurIPS},
                      year={2021}}
                

                    @inproceedings{ma2021finding,
                      title={Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks},
                      author={Ma, Chen and Guo, Xiangyu and Chen, Li and Yong, Jun-Hai and Wang, Yisen},
                      booktitle={NeurIPS},
                      year={2021}}
                

                      @inproceedings{wang2021reparameterized,
                        title={Reparameterized Sampling for Generative Adversarial Networks},
                        author={Wang, Yifei and Wang, Yisen and Yang, Jiansheng and Lin, Zhouchen},
                        booktitle={ECML-PKDD},
                        year={2021}}
                

                      @inproceedings{wang2021demystifying,
                        title={Demystifying adversarial training via a unified probabilistic framework},
                        author={Wang, Yifei and Wang, Yisen and Yang, Jiansheng and Lin, Zhouchen},
                        booktitle={ICML 2021 Workshop on Adversarial Machine Learning},
                        year={2021}}
                

                      @inproceedings{tian2021analysis,
                        title={Analysis and Applications of Class-wise Robustness in Adversarial Training},
                        author={Tian, Qi and Kuang, Kun and Jiang, Kelu and Wu, Fei and Wang, Yisen},
                        booktitle={KDD},
                        year={2021}}
                

                      @inproceedings{bai2021improving,
                        title={Improving adversarial robustness via channel-wise activation suppressing},
                        author={Bai, Yang and Zeng, Yuyuan and Jiang, Yong and Xia, Shu-Tao and Ma, Xingjun and Wang, Yisen},
                        booktitle={ICLR},
                        year={2021}}
                

                        @inproceedings{wang2021unified,
                          title={A unified approach to interpreting and boosting adversarial transferability},
                          author={Wang, Xin and Ren, Jie and Lin, Shuyun and Zhu, Xiangming and Wang, Yisen and Zhang, Quanshi},
                          booktitle={ICLR},
                          year={2021}}
                

                      @inproceedings{duan2020adversarial,
                        title={Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles},
                        author={Duan, Ranjie and Ma, Xingjun and Wang, Yisen and Bailey, James and Qin, A Kai and Yang, Yun},
                        booktitle={CVPR},
                        year={2020}}
                

                        @inproceedings{bai2020improving,
                          title={Improving query efficiency of black-box adversarial attack},
                          author={Bai, Yang and Zeng, Yuyuan and Jiang, Yong and Wang, Yisen and Xia, Shu-Tao and Guo, Weiwei},
                          booktitle={ECCV},
                          year={2020}}
                

                        @article{ma2019understanding,
                          title={Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems},
                          author={Ma, Xingjun and Niu, Yuhao and Gu, Lin and Wang, Yisen and Zhao, Yitian and Bailey, James and Lu, Feng},
                          journal={Pattern Recognition},
                          year={2020}}
                

                      @inproceedings{bai2019hilbert,
                        title={Hilbert-Based Generative Defense for Adversarial Examples},
                        author={Bai, Yang and Feng, Yan and Wang, Yisen and Dai, Tao and Xia, Shu-Tao and Jiang, Yong},
                        booktitle={ICCV},
                        year={2019}}
                

                      @inproceedings{ma2018characterizing,
                        title={Characterizing adversarial subspaces using local intrinsic dimensionality},
                        author={Ma, Xingjun and Li, Bo and Wang, Yisen and Erfani, Sarah M and Wijewickrema, Sudanthi and Schoenebeck, Grant and Song, Dawn and Houle, Michael E and Bailey, James},
                        booktitle={ICLR},
                        year={2018}}