Yisen Wang

Yisen Wang 王奕森
Assistant Professor, Ph.D. Advisor
School of EECS, Peking University


Room 2201, Science Building #2
Peking University, Beijing 100871, China


Email: yisen.wang AT pku DOT edu.cn 
Homepage: https://yisenwang.github.io/
School of EECS Official Website: http://www.cis.pku.edu.cn/info/1084/1244.htm 

AI Institute Official Website: http://www.ai.pku.edu.cn/info/1284/1643.htm

[Google Scholar] [Github]



Biography

I am now a Tenure-track Assistant Professor (Ph.D. Advisor) in Department of Machine Intelligence, School of Electronics Engineering and Computer Science (EECS), Peking University. I am also co-affiliated with the Center for Machine Learning, Institute for Artificial Intelligence, Peking University. I am also a faculty member of ZERO Lab at Peking University led by Prof. Zhouchen Lin.

I got my PhD degree from Department of Computer Science and Technology, Tsinghua University. I have visited Georgia Tech, USA, hosted by Prof. Le Song and Prof. Hongyuan Zha, and The University of Melbourne, Australia, hosted by Prof. James Bailey

My research interest broadly includes the theory and applications of machine learning and deep learning, such as adversarial learning, graph learning, and weakly/self-supervised learning.


Openings

We are recruiting highly motivated post-docs via Peking University Boya Postdoctoral Fellowship (Salary 350K+)

We are always actively recruiting Ph.D. students and interns! Welcome to contact me with your detailed CV!


News


Publications

Equal  Contribution;  Corresponding  Author)

  1. Clustering Effect of Adversarial Robust Models [PDF]
    Yang Bai, Xin Yan, Yong Jiang, Shu-Tao Xia#, Yisen Wang#
    Neural Information Processing Systems (NeurIPS 2021), 2021 (Spotlight, Top 3%)

  2. Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State [PDF]
    Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Yisen Wang, Zhouchen Lin
    Neural Information Processing Systems (NeurIPS 2021), 2021 (Spotlight, Top 3%)

  3. Dissecting the Diffusion Process in Linear Graph Convolutional Networks [PDF]
    Yifei Wang, Yisen Wang#, Jiansheng Yang, Zhouchen Lin
    Neural Information Processing Systems (NeurIPS 2021), 2021

  4. Residual Relaxation for Multi-view Representation Learning [PDF]
    Yifei Wang, Zhengyang Geng, Feng Jiang, Chuming Li, Yisen Wang#, Jiansheng Yang, Zhouchen Lin
    Neural Information Processing Systems (NeurIPS 2021), 2021

  5. Adversarial Neuron Pruning Purifies Backdoored Deep Models [PDF]
    Dongxian Wu, Yisen Wang#
    Neural Information Processing Systems (NeurIPS 2021), 2021

  6. Moire Attack (MA): A New Potential Risk of Screen Photos [PDF]
    Dantong Niu, Guo Ruohao, Yisen Wang#
    Neural Information Processing Systems (NeurIPS 2021), 2021

  7. Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks [PDF]
    Hanxun Huang, Yisen Wang#, Sarah Monazam Erfani, Quanquan Gu, James Bailey, Xingjun Ma#
    Neural Information Processing Systems (NeurIPS 2021), 2021

  8. Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness [PDF]
    Jie Ren, Die Zhang, Yisen Wang*, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, Quanshi Zhang
    Neural Information Processing Systems (NeurIPS 2021), 2021

  9. Reparameterized Sampling for Generative Adversarial Networks [PDF] [Award]
    Yifei Wang, Yisen Wang#, Jiansheng Yang, Zhouchen Lin
    European Conference on Machine Learning (ECML 2021), 2021 (Best (Student) Machine Learning Paper Award)

  10. Demystifying Adversarial Training via A Unified Probabilistic Framework [PDF] [Award]
    Yifei Wang, Yisen Wang#, Jiansheng Yang, Zhouchen Lin
    International Conference on Machine Learning Workshop (ICML Workshop 2021), 2021 (Silver Best Paper Award)

  11. Can Subnetwork Structure be the Key to Out-of-Distribution Generalization? [PDF]
    Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron Courville
    International Conference on Machine Learning (ICML 2021), 2021 (Long Talk, Top 3%)

  12. Leveraged Weighted Loss for Partial Label Learning [PDF] [Code]
    Hongwei Wen*, Jingyi Cui*, Hanyuan Hang, Jiabin Liu#, Yisen Wang#, Zhouchen Lin#
    International Conference on Machine Learning (ICML 2021), 2021 (Long Talk, Top 3%)

  13. GBHT: Gradient Boosting Histogram Transform for Density Estimation [PDF]
    Jingyi Cui*, Hanyuan Hang*, Yisen Wang#, Zhouchen Lin
    International Conference on Machine Learning (ICML 2021), 2021 

  14. Analysis and Applications of Class-wise Robustness in Adversarial Training [PDF]
    Qi Tian, Kun Kuang#, Kelu Jiang, Fei Wu, Yisen Wang#
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021), 2021 

  15. Unlearnable Examples: Making Personal Data Unexploitable [PDF] [Code] [MIT Technology Review]
    Hanxun Huang, Xingjun Ma#, Sarah Monazam Erfani, James Bailey, Yisen Wang#
    International Conference on Learning Representations (ICLR 2021), 2021 (Spotlight, Top 4%)

  16. Improving Adversarial Robustness via Channel-wise Activation Suppressing [PDF] [Code]
    Yang Bai*, Yuyuan Zeng*, Yong Jiang, Shu-Tao Xia#, Xingjun Ma, Yisen Wang#
    International Conference on Learning Representations (ICLR 2021), 2021 (Spotlight, Top 4%)

  17. Towards A Unified Understanding and Improving of Adversarial Transferability [PDF] [Code]
    Xin Wang*, Jie Ren*, Shuyun Lin, Xiangming Zhu, Yisen Wang, Quanshi Zhang
    International Conference on Learning Representations (ICLR 2021), 2021

  18. Adversarial Weight Perturbation Helps Robust Generalization [PDF] [Code] [NeurIPS MeetUp Video]
    Dongxian Wu, Shu-Tao Xia, Yisen Wang#
    Neural Information Processing Systems (NeurIPS 2020), 2020 

  19. Normalized Loss Functions for Deep Learning with Noisy Labels [PDF] [Code]
    Xingjun Ma*, Hanxun Huang*, Yisen Wang#, Simone Romano, Sarah Erfani and James Bailey
    International Conference on Machine Learning (ICML 2020), 2020 

  20. Improving Adversarial Robustness Requires Revisiting Misclassified Examples [PDF] [Code]
    Yisen Wang*, Difan Zou*, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu
    International Conference on Learning Representations (ICLR 2020), Addis Ababa, Ethiopia, 2020 

  21. Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets [PDF] [Code]
    Dongxian Wu, Yisen Wang#, Shu-Tao Xia, James Bailey, Xingjun Ma
    International Conference on Learning Representations (ICLR 2020), Addis Ababa, Ethiopia, 2020 (Spotlight)

  22. Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles [PDF] [Code]
    Ranjie Duan, Xingjun Ma, Yisen Wang, James Bailey, Kai Qin, Yun Yang
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020), Seattle, USA, 2020 

  23. Improving Query Efficiency of Black-box Adversarial Attack [PDF] [Code]
    Yang Bai*, Yuyuan Zeng*, Yong Jiang#, Yisen Wang#, Shu-Tao Xia, Weiwei Guo
    European Conference on Computer Vision (ECCV 2020), 2020 

  24. Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems [PDF]
    Xingjun Ma, Yuhao Niu, Lin Gu, Yisen Wang, Yitian Zhao, James Bailey, Feng Lu
    Pattern Recognition (PR), 2020

  25. On the Convergence and Robustness of Adversarial Training [PDF] [Code]
    Yisen Wang*, Xingjun Ma*, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu
    International Conference on Machine Learning (ICML 2019), Long Beach, USA, 2019 (Long Talk)

  26. Symmetric Cross Entropy for Robust Learning with Noisy Labels [PDF] [Code]
    Yisen Wang*, Xingjun Ma*, Zaiyi Chen, Yuan Luo, Jinfeng Yi, James Bailey
    International Conference on Computer Vision (ICCV 2019), Seoul, Korea, 2019 

  27. Hilbert-Based Generative Defense for Adversarial Examples [PDF]
    Yang Bai*, Yan Feng*, Yisen Wang#, Shu-Tao Xia, Yong Jiang#
    International Conference on Computer Vision (ICCV 2019), Seoul, Korea, 2019 

  28. Dirichlet Latent Variable Hierarchical Recurrent Encoder-Decoder in Dialogue Generation [PDF] [Code]
    Min Zeng, Yisen Wang#, Yuan Luo
    Conference on Empirical Methods in Natural Language Processing (EMNLP 2019), Hong Kong, China, 2019 

  29. Learning Deep Hidden Nonlinear Dynamics from Aggregate Data [PDF][Appendix]
    Yisen Wang, Bo Dai, Lingkai Kong, Sarah Monazam Erfani, James Bailey, Hongyuan Zha
    Conference on Uncertainty in Artificial Intelligence (UAI 2018), California, USA, 2018

  30. Dimensionality-Driven Learning with Noisy Labels [PDF] [Code]
    Xingjun Ma*, Yisen Wang*, Michael E. Houle, Shuo Zhou, Sarah Monazam Erfani, Shu-Tao Xia, Sudanthi Wijewickrema, James Bailey
    International Conference on Machine Learning (ICML 2018), Stockholm, Sweden, 2018 (Long Talk)

  31. Iterative Learning with Open-set Noisy Labels [PDF] [Code]
    Yisen Wang, Weiyang Liu, Xingjun Ma, James Bailey, Hongyuan Zha, Le Song, Shu-Tao Xia
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, USA, 2018 (Spotlight)

  32. Decoupled Networks [PDF] [Code]
    Weiyang Liu, Zhen Liu, Zhiding Yu, Bo Dai, Rongmei Lin, Yisen Wang, James Rehg, Le Song
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, USA, 2018 (Spotlight)

  33. Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality [PDF] [Code]
    Xingjun Ma, Bo Li, Yisen Wang, Sarah M. Erfani, Sudanthi Wijewickrema, Michael E. Houle, Grant Schoenebeck, Dawn Song, James Bailey
    International Conference on Learning Representations (ICLR 2018), Vancouver, BC, Canada, 2018 (Oral)

  34. A Novel Consistent Random Forest Framework: Bernoulli Random Forests [PDF]
    Yisen Wang, Shu-Tao Xia, Qingtao Tang, Jia Wu, Xingquan Zhu
    IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2017

  35. Residual convolutional CTC networks for automatic speech recognition [PDF]
    Yisen Wang*, Xuejiao Deng*, Songpai Pu, Zhiheng Huang
    Arxiv Technical Report, 2017

  36. Unbiased Multivariate Correlation Analysis [PDF] [Appendix]
    Yisen Wang, Simone Romano, Nguyen Vinh, James Bailey, Xingjun Ma, Shu-Tao Xia
    AAAI Conference on Artificial Intelligence (AAAI 2017), San Francisco, California, USA, 2017. (Oral)

  37. Unifying Attribute Splitting Criteria of Decision Trees by Tsallis Entropy [PDF]
    Yisen Wang, Shu-Tao Xia
    IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017), New Orleans, USA, 2017. 

  38. Student-t Process Regression with Student-t Likelihood [PDF]
    Qingtao Tang, Li Niu, Yisen Wang, Tao Dai, Wangpeng An, Jianfei Cai, Shu-Tao Xia
    International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne, Australia, 2017. (Oral)

  39. Robust Survey Aggregation with Student-t Distribution and Sparse Representation [PDF]
    Qingtao Tang, Tao Dai, Li Niu, Yisen Wang, Shu-Tao Xia, Jianfei Cai
    International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne, Australia, 2017. (Oral)

  40. Bernoulli Random Forests: Closing the Gap between Theoretical Consistency and Empirical Soundness [PDF]
    Yisen Wang, Qingtao Tang, Shu-Tao Xia, Jia Wu, Xingquan Zhu
    International Joint Conference on Artificial Intelligence (IJCAI 2016), New York, USA, 2016. (Oral)

  41. Student-t Process Regression with Dependent Student-t Noise [PDF]
    Qingtao Tang, Yisen Wang, Shu-Tao Xia
    European Conference on Artificial Intelligence (ECAI 2016), Hague, Netherlands, 2016. (Oral)


Academic Service

Talks


Awards