教育背景

2018年博士毕业于清华大学计算机系

主要研究领域

机器学习，深度学习，对抗学习，图学习，弱监督学习

代表性学术论著

1.Dongxian Wu, Shu-Tao Xia,Yisen Wang#; “Adversarial Weight Perturbation Helps Robust Generalization”, Neural Information Processing Systems (NeurIPS 2020)

2.Yisen Wang*, Xingjun Ma*, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu; “On the Convergence and Robustness of Adversarial Training”, International Conference on Machine Learning (ICML 2019)

3.Yisen Wang*, Difan Zou*, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu; “Improving Adversarial Robustness Requires Revisiting Misclassified Examples”, International Conference on Learning Representations (ICLR 2020)

4.Dongxian Wu,Yisen Wang#, Shu-Tao Xia, James Bailey, Xingjun Ma; “Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets”, International Conference on Learning Representations (ICLR 2020)

5.Xingjun Ma*, Hanxun Huang*,Yisen Wang#, Simone Romano, Sarah Erfani, James Bailey; “Normalized Loss Functions for Deep Learning with Noisy Labels”, International Conference on Machine Learning (ICML 2020)

6.Yisen Wang*, Xingjun Ma*, Zaiyi Chen, Yuan Luo, Jinfeng Yi, James Bailey; “Symmetric Cross Entropy for Robust Learning with Noisy Labels”, International Conference on Computer Vision (ICCV 2019)

7.Yisen Wang, Weiyang Liu, Xingjun Ma, James Bailey, Hongyuan Zha, Le Song, Shu-Tao Xia; “Iterative Learning with Open-set Noisy Labels”, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018)