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ORCIDYisen Wang 0001
Person information
- affiliation: Peking University, Institute for Artificial Intelligence, School of Intelligence Science and Technology, Beijing, China
- affiliation (former): Shanghai Jiao Tong University, Department of Computer Science and Engineering, China
- affiliation (PhD 2018): Tsinghua University, Beijing, China
Other persons with the same name
- Yisen Wang
- Yisen Wang 0002
![[0000-0002-0181-2137]](https://dblp.org/img/orcid-mark.12x12.png "0000-0002-0181-2137")
— State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China - Yisen Wang 0003 — Nanjing University of Aeronautics and Astronautics, China
- Yisen Wang 0004 — Lanzhou University, Lanzhou Center for Theoretical Physics, China
- Yisen Wang 0005 — Beihang University, School of Electronic and Information Engineering, Beijing, China
- Yisen Wang 0006 — University of Wisconsin-Madison, Department of Mechanical Engineering, WI, USA
- Yisen Wang 0007 — KLMM, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China (and 1 more)
- Yisen Wang 0008 — Nanjing University, State Key Laboratory for Novel Software Technology, China
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2020 – today
- 2024
[j13]Shen Yan, Qingyan Meng, Mingqing Xiao, Yisen Wang, Zhouchen Lin:
Sampling complex topology structures for spiking neural networks. Neural Networks 172: 106121 (2024)- [j12]Wenbin Ouyang, Yisen Wang, Paul Weng, Shaochen Han:
Generalization in Deep RL for TSP Problems via Equivariance and Local Search. SN Comput. Sci. 5(4): 369 (2024) - [c84]Tianqi Du, Yifei Wang, Yisen Wang:
On the Role of Discrete Tokenization in Visual Representation Learning. ICLR 2024 - [c83]Yifei Wang, Jizhe Zhang, Yisen Wang:
Do Generated Data Always Help Contrastive Learning? ICLR 2024 - [c82]Yifei Wang, Qi Zhang, Yaoyu Guo, Yisen Wang:
Non-negative Contrastive Learning. ICLR 2024 - [c81]Ang Li, Yichuan Mo, Mingjie Li, Yisen Wang:
PID: Prompt-Independent Data Protection Against Latent Diffusion Models. ICML 2024 - [c80]Yichuan Mo, Hui Huang, Mingjie Li, Ang Li, Yisen Wang:
TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors. ICML 2024 - [c79]Qi Zhang, Tianqi Du, Haotian Huang, Yifei Wang, Yisen Wang:
Look Ahead or Look Around? A Theoretical Comparison Between Autoregressive and Masked Pretraining. ICML 2024 - [i85]Yichuan Mo, Yuji Wang, Zeming Wei, Yisen Wang:
Studious Bob Fight Back Against Jailbreaking via Prompt Adversarial Tuning. CoRR abs/2402.06255 (2024) - [i84]Yifei Wang, Jizhe Zhang, Yisen Wang:
Do Generated Data Always Help Contrastive Learning? CoRR abs/2403.12448 (2024) - [i83]Yifei Wang, Qi Zhang, Yaoyu Guo, Yisen Wang:
Non-negative Contrastive Learning. CoRR abs/2403.12459 (2024) - [i82]Jinmin Li, Kuofeng Gao, Yang Bai, Jingyun Zhang, Shutao Xia, Yisen Wang:
FMM-Attack: A Flow-based Multi-modal Adversarial Attack on Video-based LLMs. CoRR abs/2403.13507 (2024) - [i81]Yifei Wang, Wenhan Ma, Stefanie Jegelka, Yisen Wang:
How to Craft Backdoors with Unlabeled Data Alone? CoRR abs/2404.06694 (2024) - [i80]George Ma, Yifei Wang, Derek Lim, Stefanie Jegelka, Yisen Wang:
A Canonization Perspective on Invariant and Equivariant Learning. CoRR abs/2405.18378 (2024) - [i79]Yifei Wang, Yuyang Wu, Zeming Wei, Stefanie Jegelka, Yisen Wang:
A Theoretical Understanding of Self-Correction through In-context Alignment. CoRR abs/2405.18634 (2024) - [i78]Ang Li, Yichuan Mo, Mingjie Li, Yisen Wang:
PID: Prompt-Independent Data Protection Against Latent Diffusion Models. CoRR abs/2406.15305 (2024) - [i77]Qi Zhang, Tianqi Du, Haotian Huang, Yifei Wang, Yisen Wang:
Look Ahead or Look Around? A Theoretical Comparison Between Autoregressive and Masked Pretraining. CoRR abs/2407.00935 (2024) - [i76]Tianqi Du, Yifei Wang, Yisen Wang:
On the Role of Discrete Tokenization in Visual Representation Learning. CoRR abs/2407.09087 (2024) - [i75]Yichuan Mo, Hui Huang, Mingjie Li, Ang Li, Yisen Wang:
TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors. CoRR abs/2409.05294 (2024) - [i74]Lexiang Hu, Yisen Wang, Zhouchen Lin:
EKAN: Equivariant Kolmogorov-Arnold Networks. CoRR abs/2410.00435 (2024) - [i73]Yisen Wang, Yichuan Mo, Dongxian Wu, Mingjie Li, Xingjun Ma, Zhouchen Lin:
On the Adversarial Transferability of Generalized "Skip Connections". CoRR abs/2410.08950 (2024) - [i72]Qi Zhang, Yifei Wang, Jingyi Cui, Xiang Pan, Qi Lei, Stefanie Jegelka, Yisen Wang:
Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness. CoRR abs/2410.21331 (2024) - [i71]Lizhe Fang, Yifei Wang, Zhaoyang Liu, Chenheng Zhang, Stefanie Jegelka, Jinyang Gao, Bolin Ding, Yisen Wang:
What is Wrong with Perplexity for Long-context Language Modeling? CoRR abs/2410.23771 (2024) - 2023
- [j11]Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Yisen Wang, Zhouchen Lin:
SPIDE: A purely spike-based method for training feedback spiking neural networks. Neural Networks 161: 9-24 (2023) - [j10]Yang Bai, Yisen Wang, Yuyuan Zeng, Yong Jiang, Shu-Tao Xia:
Query efficient black-box adversarial attack on deep neural networks. Pattern Recognit. 133: 109037 (2023) - [j9]Qi Chen, Yifei Wang, Zhengyang Geng, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Equilibrium Image Denoising With Implicit Differentiation. IEEE Trans. Image Process. 32: 1868-1881 (2023) - [c78]Shiji Xin, Yifei Wang, Jingtong Su, Yisen Wang:
On the Connection between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization. AAAI 2023: 10519-10527 - [c77]Zeming Wei, Yifei Wang, Yiwen Guo, Yisen Wang:
CFA: Class-Wise Calibrated Fair Adversarial Training. CVPR 2023: 8193-8201 - [c76]Hongjun Wang, Yisen Wang:
Generalist: Decoupling Natural and Robust Generalization. CVPR 2023: 20554-20563 - [c75]Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo:
Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks. ICCV 2023: 6143-6153 - [c74]Yifei Wang, Qi Zhang, Tianqi Du, Jiansheng Yang, Zhouchen Lin, Yisen Wang:
A Message Passing Perspective on Learning Dynamics of Contrastive Learning. ICLR 2023 - [c73]Xiaojun Guo, Yifei Wang, Tianqi Du, Yisen Wang:
ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond. ICLR 2023 - [c72]Mingjie Li, Yifei Wang, Yisen Wang, Zhouchen Lin:
Unbiased Stochastic Proximal Solver for Graph Neural Networks with Equilibrium States. ICLR 2023 - [c71]Rundong Luo, Yifei Wang, Yisen Wang:
Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning. ICLR 2023 - [c70]Xuyang Zhao, Tianqi Du, Yisen Wang, Jun Yao, Weiran Huang:
ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations. ICLR 2023 - [c69]Zhijian Zhuo, Yifei Wang, Jinwen Ma, Yisen Wang:
Towards a Unified Theoretical Understanding of Non-contrastive Learning via Rank Differential Mechanism. ICLR 2023 - [c68]Jingyi Cui, Weiran Huang, Yifei Wang, Yisen Wang:
Rethinking Weak Supervision in Helping Contrastive Learning. ICML 2023: 6448-6467 - [c67]Qi Zhang, Yifei Wang, Yisen Wang:
On the Generalization of Multi-modal Contrastive Learning. ICML 2023: 41677-41693 - [c66]Xiaojun Guo, Yifei Wang, Zeming Wei, Yisen Wang:
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning. NeurIPS 2023 - [c65]Mingjie Li, Yisen Wang, Zhouchen Lin:
GEQ: Gaussian Kernel Inspired Equilibrium Models. NeurIPS 2023 - [c64]Ang Li, Yifei Wang, Yiwen Guo, Yisen Wang:
Adversarial Examples Are Not Real Features. NeurIPS 2023 - [c63]George Ma, Yifei Wang, Yisen Wang:
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding. NeurIPS 2023 - [c62]Yifei Wang, Liangchen Li, Jiansheng Yang, Zhouchen Lin, Yisen Wang:
Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective. NeurIPS 2023 - [c61]Qi Zhang, Yifei Wang, Yisen Wang:
Identifiable Contrastive Learning with Automatic Feature Importance Discovery. NeurIPS 2023 - [i70]Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Yisen Wang, Zhouchen Lin:
SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks. CoRR abs/2302.00232 (2023) - [i69]Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo:
Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks. CoRR abs/2302.14311 (2023) - [i68]Xuyang Zhao, Tianqi Du, Yisen Wang, Jun Yao, Weiran Huang:
ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations. CoRR abs/2303.01092 (2023) - [i67]Rundong Luo, Yifei Wang, Yisen Wang:
Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning. CoRR abs/2303.01289 (2023) - [i66]Zhijian Zhuo, Yifei Wang, Jinwen Ma, Yisen Wang:
Towards a Unified Theoretical Understanding of Non-contrastive Learning via Rank Differential Mechanism. CoRR abs/2303.02387 (2023) - [i65]Yifei Wang, Qi Zhang, Tianqi Du, Jiansheng Yang, Zhouchen Lin, Yisen Wang:
A Message Passing Perspective on Learning Dynamics of Contrastive Learning. CoRR abs/2303.04435 (2023) - [i64]Xiaojun Guo, Yifei Wang, Tianqi Du, Yisen Wang:
ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond. CoRR abs/2303.06562 (2023) - [i63]Hongjun Wang, Yisen Wang:
Generalist: Decoupling Natural and Robust Generalization. CoRR abs/2303.13813 (2023) - [i62]Zeming Wei, Yifei Wang, Yiwen Guo, Yisen Wang:
CFA: Class-wise Calibrated Fair Adversarial Training. CoRR abs/2303.14460 (2023) - [i61]Jingyi Cui, Weiran Huang, Yifei Wang, Yisen Wang:
Rethinking Weak Supervision in Helping Contrastive Learning. CoRR abs/2306.04160 (2023) - [i60]Qi Zhang, Yifei Wang, Yisen Wang:
On the Generalization of Multi-modal Contrastive Learning. CoRR abs/2306.04272 (2023) - [i59]Hong Zhu, Runpeng Yu, Xing Tang, Yifei Wang, Yuan Fang, Yisen Wang:
Robust Long-Tailed Learning via Label-Aware Bounded CVaR. CoRR abs/2308.15405 (2023) - [i58]Zeming Wei, Yifei Wang, Yisen Wang:
Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations. CoRR abs/2310.06387 (2023) - [i57]Chen Liu, Hongyu Zang, Xin Li, Yong Heng, Yifei Wang, Zhen Fang, Yisen Wang, Mingzhong Wang:
Towards Control-Centric Representations in Reinforcement Learning from Images. CoRR abs/2310.16655 (2023) - [i56]Jiangyan Ma, Yifei Wang, Yisen Wang:
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding. CoRR abs/2310.18716 (2023) - [i55]Qi Zhang, Yifei Wang, Yisen Wang:
Identifiable Contrastive Learning with Automatic Feature Importance Discovery. CoRR abs/2310.18904 (2023) - [i54]Ang Li, Yifei Wang, Yiwen Guo, Yisen Wang:
Adversarial Examples Are Not Real Features. CoRR abs/2310.18936 (2023) - [i53]Yifei Wang, Liangchen Li, Jiansheng Yang, Zhouchen Lin, Yisen Wang:
Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective. CoRR abs/2310.19360 (2023) - [i52]Xiaojun Guo, Yifei Wang, Zeming Wei, Yisen Wang:
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning. CoRR abs/2311.02687 (2023) - 2022
- [j8]Qingyan Meng, Shen Yan, Mingqing Xiao, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo:
Training much deeper spiking neural networks with a small number of time-steps. Neural Networks 153: 254-268 (2022) - [c60]Qi Chen, Yifei Wang, Yisen Wang, Jianlong Chang, Qi Tian, Jiansheng Yang, Zhouchen Lin:
Efficient and Scalable Implicit Graph Neural Networks with Virtual Equilibrium. IEEE Big Data 2022: 864-873 - [c59]Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo:
Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation. CVPR 2022: 12434-12443 - [c58]Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training. ICLR 2022 - [c57]Yifei Wang, Qi Zhang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap. ICLR 2022 - [c56]Mingjie Li, Yisen Wang, Xingyu Xie, Zhouchen Lin:
Optimization inspired Multi-Branch Equilibrium Models. ICLR 2022 - [c55]Hongjun Wang, Yisen Wang:
Self-ensemble Adversarial Training for Improved Robustness. ICLR 2022 - [c54]Qi Chen, Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Optimization-Induced Graph Implicit Nonlinear Diffusion. ICML 2022: 3648-3661 - [c53]Yiwen Kou, Qinyuan Zheng, Yisen Wang:
Certified Adversarial Robustness Under the Bounded Support Set. ICML 2022: 11559-11597 - [c52]Mingjie Li, Xiaojun Guo, Yifei Wang, Yisen Wang, Zhouchen Lin:
G2CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters. ICML 2022: 12782-12796 - [c51]Mingjie Li, Yisen Wang, Zhouchen Lin:
CerDEQ: Certifiable Deep Equilibrium Model. ICML 2022: 12998-13013 - [c50]Yichuan Mo, Dongxian Wu, Yifei Wang, Yiwen Guo, Yisen Wang:
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture. NeurIPS 2022 - [c49]Qixun Wang, Yifei Wang, Hong Zhu, Yisen Wang:
Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors. NeurIPS 2022 - [c48]Qi Zhang, Yifei Wang, Yisen Wang:
How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders. NeurIPS 2022 - [i51]Hongjun Wang, Yisen Wang:
Self-Ensemble Adversarial Training for Improved Robustness. CoRR abs/2203.09678 (2022) - [i50]Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training. CoRR abs/2203.13455 (2022) - [i49]Yifei Wang, Qi Zhang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap. CoRR abs/2203.13457 (2022) - [i48]Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo:
Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation. CoRR abs/2205.00459 (2022) - [i47]Qi Chen, Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Optimization-Induced Graph Implicit Nonlinear Diffusion. CoRR abs/2206.14418 (2022) - [i46]Quanshi Zhang, Xin Wang, Jie Ren, Xu Cheng, Shuyun Lin, Yisen Wang, Xiangming Zhu:
Proving Common Mechanisms Shared by Twelve Methods of Boosting Adversarial Transferability. CoRR abs/2207.11694 (2022) - [i45]Qixun Wang, Yifei Wang, Hong Zhu, Yisen Wang:
Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors. CoRR abs/2210.06807 (2022) - [i44]Yichuan Mo, Dongxian Wu, Yifei Wang, Yiwen Guo, Yisen Wang:
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture. CoRR abs/2210.07540 (2022) - [i43]Qi Zhang, Yifei Wang, Yisen Wang:
How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders. CoRR abs/2210.08344 (2022) - [i42]Shiji Xin, Yifei Wang, Jingtong Su, Yisen Wang:
On the Connection between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization. CoRR abs/2212.09082 (2022) - 2021
- [j7]Xingjun Ma, Yuhao Niu, Lin Gu, Yisen Wang, Yitian Zhao, James Bailey, Feng Lu:
Understanding adversarial attacks on deep learning based medical image analysis systems. Pattern Recognit. 110: 107332 (2021) - [c47]Yang Bai, Yuyuan Zeng, Yong Jiang, Shu-Tao Xia, Xingjun Ma, Yisen Wang:
Improving Adversarial Robustness via Channel-wise Activation Suppressing. ICLR 2021 - [c46]Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, Yisen Wang:
Unlearnable Examples: Making Personal Data Unexploitable. ICLR 2021 - [c45]Xin Wang, Jie Ren, Shuyun Lin, Xiangming Zhu, Yisen Wang, Quanshi Zhang:
A Unified Approach to Interpreting and Boosting Adversarial Transferability. ICLR 2021 - [c44]Jingyi Cui, Hanyuan Hang, Yisen Wang, Zhouchen Lin:
GBHT: Gradient Boosting Histogram Transform for Density Estimation. ICML 2021: 2233-2243 - [c43]Hongwei Wen, Jingyi Cui, Hanyuan Hang, Jiabin Liu, Yisen Wang, Zhouchen Lin:
Leveraged Weighted Loss for Partial Label Learning. ICML 2021: 11091-11100 - [c42]Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron C. Courville:
Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization? ICML 2021: 12356-12367 - [c41]Qi Tian, Kun Kuang, Kelu Jiang, Fei Wu, Yisen Wang:
Analysis and Applications of Class-wise Robustness in Adversarial Training. KDD 2021: 1561-1570 - [c40]Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, Quanshi Zhang:
Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness. NeurIPS 2021: 3797-3810 - [c39]Lingshen He, Yuxuan Chen, Zhengyang Shen, Yiming Dong, Yisen Wang, Zhouchen Lin:
Efficient Equivariant Network. NeurIPS 2021: 5290-5302 - [c38]Hanxun Huang, Yisen Wang, Sarah M. Erfani, Quanquan Gu, James Bailey, Xingjun Ma:
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks. NeurIPS 2021: 5545-5559 - [c37]Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Dissecting the Diffusion Process in Linear Graph Convolutional Networks. NeurIPS 2021: 5758-5769 - [c36]Yifei Wang, Zhengyang Geng, Feng Jiang, Chuming Li, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Residual Relaxation for Multi-view Representation Learning. NeurIPS 2021: 12104-12115 - [c35]Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Yisen Wang, Zhouchen Lin:
Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State. NeurIPS 2021: 14516-14528 - [c34]Dongxian Wu, Yisen Wang:
Adversarial Neuron Pruning Purifies Backdoored Deep Models. NeurIPS 2021: 16913-16925 - [c33]Chen Ma, Xiangyu Guo, Li Chen, Jun-Hai Yong, Yisen Wang:
Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks. NeurIPS 2021: 19288-19300 - [c32]Zhengyang Geng, Xin-Yu Zhang, Shaojie Bai, Yisen Wang, Zhouchen Lin:
On Training Implicit Models. NeurIPS 2021: 24247-24260 - [c31]Dantong Niu, Ruohao Guo, Yisen Wang:
Morié Attack (MA): A New Potential Risk of Screen Photos. NeurIPS 2021: 26117-26129 - [c30]Lingshen He, Yiming Dong, Yisen Wang, Dacheng Tao, Zhouchen Lin:
Gauge Equivariant Transformer. NeurIPS 2021: 27331-27343 - [c29]Yang Bai, Xin Yan, Yong Jiang, Shu-Tao Xia, Yisen Wang:
Clustering Effect of Adversarial Robust Models. NeurIPS 2021: 29590-29601 - [c28]Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Reparameterized Sampling for Generative Adversarial Networks. ECML/PKDD (3) 2021: 494-509 - [c27]Wenbin Ouyang, Yisen Wang, Shaochen Han, Zhejian Jin, Paul Weng:
Improving Generalization of Deep Reinforcement Learning-based TSP Solvers. SSCI 2021: 1-8 - [i41]Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, Yisen Wang:
Unlearnable Examples: Making Personal Data Unexploitable. CoRR abs/2101.04898 (2021) - [i40]Nodens Koren, Qiuhong Ke, Yisen Wang, James Bailey, Xingjun Ma:
Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions. CoRR abs/2101.06704 (2021) - [i39]Shihao Zhao, Xingjun Ma, Yisen Wang, James Bailey, Bo Li, Yu-Gang Jiang:
What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space. CoRR abs/2101.06898 (2021) - [i38]Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Dissecting the Diffusion Process in Linear Graph Convolutional Networks. CoRR abs/2102.10739 (2021) - [i37]Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Xu Cheng, Xin Wang, Yiting Chen, Jie Shi, Quanshi Zhang:
Game-theoretic Understanding of Adversarially Learned Features. CoRR abs/2103.07364 (2021) - [i36]Yang Bai, Yuyuan Zeng, Yong Jiang, Shu-Tao Xia, Xingjun Ma, Yisen Wang:
Improving Adversarial Robustness via Channel-wise Activation Suppressing. CoRR abs/2103.08307 (2021) - [i35]Xingyu Xie, Qiuhao Wang, Zenan Ling, Xia Li, Yisen Wang, Guangcan Liu, Zhouchen Lin:
Optimization Induced Equilibrium Networks. CoRR abs/2105.13228 (2021) - [i34]Qi Tian, Kun Kuang, Kelu Jiang, Fei Wu, Yisen Wang:
Analysis and Applications of Class-wise Robustness in Adversarial Training. CoRR abs/2105.14240 (2021) - [i33]Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron C. Courville:
Can Subnetwork Structure be the Key to Out-of-Distribution Generalization? CoRR abs/2106.02890 (2021) - [i32]Hongwei Wen, Jingyi Cui, Hanyuan Hang, Jiabin Liu, Yisen Wang, Zhouchen Lin:
Leveraged Weighted Loss for Partial Label Learning. CoRR abs/2106.05731 (2021) - [i31]Jingyi Cui, Hanyuan Hang, Yisen Wang, Zhouchen Lin:
GBHT: Gradient Boosting Histogram Transform for Density Estimation. CoRR abs/2106.05738 (2021) - [i30]Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Reparameterized Sampling for Generative Adversarial Networks. CoRR abs/2107.00352 (2021) - [i29]Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Yisen Wang, Zhouchen Lin:
Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State. CoRR abs/2109.14247 (2021) - [i28]Wenbin Ouyang, Yisen Wang, Shaochen Han, Zhejian Jin, Paul Weng:
Improving Generalization of Deep Reinforcement Learning-based TSP Solvers. CoRR abs/2110.02843 (2021) - [i27]Wenbin Ouyang, Yisen Wang, Paul Weng, Shaochen Han:
Generalization in Deep RL for TSP Problems via Equivariance and Local Search. CoRR abs/2110.03595 (2021) - [i26]Hanxun Huang, Yisen Wang, Sarah Monazam Erfani, Quanquan Gu, James Bailey, Xingjun Ma:
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks. CoRR abs/2110.03825 (2021) - [i25]Dantong Niu, Ruohao Guo, Yisen Wang:
Moiré Attack (MA): A New Potential Risk of Screen Photos. CoRR abs/2110.10444 (2021) - [i24]Dongxian Wu, Yisen Wang:
Adversarial Neuron Pruning Purifies Backdoored Deep Models. CoRR abs/2110.14430 (2021) - [i23]Yifei Wang, Zhengyang Geng, Feng Jiang, Chuming Li, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Residual Relaxation for Multi-view Representation Learning. CoRR abs/2110.15348 (2021) - [i22]Zhengyang Geng, Xin-Yu Zhang, Shaojie Bai, Yisen Wang, Zhouchen Lin:
On Training Implicit Models. CoRR abs/2111.05177 (2021) - [i21]Chen Ma, Xiangyu Guo, Li Chen, Jun-Hai Yong, Yisen Wang:
Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks. CoRR abs/2111.07492 (2021) - [i20]Zhirui Wang, Yifei Wang, Yisen Wang:
Fooling Adversarial Training with Inducing Noise. CoRR abs/2111.10130 (2021) - [i19]Yang Bai, Xin Yan, Yong Jiang, Shu-Tao Xia, Yisen Wang:
Clustering Effect of (Linearized) Adversarial Robust Models. CoRR abs/2111.12922 (2021) - [i18]Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu:
On the Convergence and Robustness of Adversarial Training. CoRR abs/2112.08304 (2021) - 2020
- [c26]Ranjie Duan, Xingjun Ma, Yisen Wang, James Bailey, A. Kai Qin, Yun Yang:
Adversarial Camouflage: Hiding Physical-World Attacks With Natural Styles. CVPR 2020: 997-1005 - [c25]Yang Bai, Yuyuan Zeng, Yong Jiang, Yisen Wang, Shu-Tao Xia, Weiwei Guo:
Improving Query Efficiency of Black-Box Adversarial Attack. ECCV (25) 2020: 101-116 - [c24]Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu:
Improving Adversarial Robustness Requires Revisiting Misclassified Examples. ICLR 2020 - [c23]Dongxian Wu, Yisen Wang, Shu-Tao Xia, James Bailey, Xingjun Ma:
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets. ICLR 2020 - [c22]Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah M. Erfani, James Bailey:
Normalized Loss Functions for Deep Learning with Noisy Labels. ICML 2020: 6543-6553 - [c21]Siyu Fan, Yisen Wang, Yuan Luo, Alexander Schmitt, Shenghua Yu:
Improving Gravitational Wave Detection with 2D Convolutional Neural Networks. ICPR 2020: 7103-7110 - [c20]Dongxian Wu, Yisen Wang, Zhuobin Zheng, Shu-Tao Xia:
Temporal Calibrated Regularization for Robust Noisy Label Learning. IJCNN 2020: 1-8 - [c19]Dongxian Wu, Shu-Tao Xia, Yisen Wang:
Adversarial Weight Perturbation Helps Robust Generalization. NeurIPS 2020 - [i17]Dongxian Wu, Yisen Wang, Shu-Tao Xia, James Bailey, Xingjun Ma:
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets. CoRR abs/2002.05990 (2020) - [i16]Ranjie Duan, Xingjun Ma, Yisen Wang, James Bailey, A. Kai Qin, Yun Yang:
Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles. CoRR abs/2003.08757 (2020) - [i15]Dongxian Wu, Yisen Wang, Shutao Xia:
Revisiting Loss Landscape for Adversarial Robustness. CoRR abs/2004.05884 (2020) - [i14]Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah M. Erfani, James Bailey:
Normalized Loss Functions for Deep Learning with Noisy Labels. CoRR abs/2006.13554 (2020) - [i13]Dongxian Wu, Yisen Wang, Zhuobin Zheng, Shutao Xia:
Temporal Calibrated Regularization for Robust Noisy Label Learning. CoRR abs/2007.00240 (2020) - [i12]Yang Bai, Yuyuan Zeng, Yong Jiang, Yisen Wang, Shu-Tao Xia, Weiwei Guo:
Improving Query Efficiency of Black-box Adversarial Attack. CoRR abs/2009.11508 (2020) - [i11]Xin Wang, Jie Ren, Shuyun Lin, Xiangming Zhu, Yisen Wang, Quanshi Zhang:
A Unified Approach to Interpreting and Boosting Adversarial Transferability. CoRR abs/2010.04055 (2020)
2010 – 2019
- 2019
- [j6]Yongbing Zhang, Yulun Zhang, Jian Zhang, Dong Xu, Yun Fu, Yisen Wang, Xiangyang Ji, Qionghai Dai:
Collaborative Representation Cascade for Single-Image Super-Resolution. IEEE Trans. Syst. Man Cybern. Syst. 49(5): 845-860 (2019) - [c18]Min Zeng, Yisen Wang, Yuan Luo:
Dirichlet Latent Variable Hierarchical Recurrent Encoder-Decoder in Dialogue Generation. EMNLP/IJCNLP (1) 2019: 1267-1272 - [c17]Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, James Bailey:
Symmetric Cross Entropy for Robust Learning With Noisy Labels. ICCV 2019: 322-330 - [c16]Yang Bai, Yan Feng, Yisen Wang, Tao Dai, Shutao Xia, Yong Jiang:
Hilbert-Based Generative Defense for Adversarial Examples. ICCV 2019: 4783-4792 - [c15]Pin Fang, Yisen Wang, Yuan Luo:
Self-Attentive Networks for one-shot Image Recognition. ICME 2019: 934-939 - [c14]Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu:
On the Convergence and Robustness of Adversarial Training. ICML 2019: 6586-6595 - [i10]Dongdong Chen, Yisen Wang, Jinfeng Yi, Zaiyi Chen, Zhi-Hua Zhou:
Joint Semantic Domain Alignment and Target Classifier Learning for Unsupervised Domain Adaptation. CoRR abs/1906.04053 (2019) - [i9]Xingjun Ma, Yuhao Niu, Lin Gu, Yisen Wang, Yitian Zhao, James Bailey, Feng Lu:
Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems. CoRR abs/1907.10456 (2019) - [i8]Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, James Bailey:
Symmetric Cross Entropy for Robust Learning with Noisy Labels. CoRR abs/1908.06112 (2019) - 2018
- [j5]Yisen Wang, Shu-Tao Xia, Qingtao Tang, Jia Wu, Xingquan Zhu:
A Novel Consistent Random Forest Framework: Bernoulli Random Forests. IEEE Trans. Neural Networks Learn. Syst. 29(8): 3510-3523 (2018) - [c13]Weiyang Liu, Zhen Liu, Zhiding Yu, Bo Dai, Rongmei Lin, Yisen Wang, James M. Rehg, Le Song:
Decoupled Networks. CVPR 2018: 2771-2779 - [c12]Yisen Wang, Weiyang Liu, Xingjun Ma, James Bailey, Hongyuan Zha, Le Song, Shu-Tao Xia:
Iterative Learning With Open-Set Noisy Labels. CVPR 2018: 8688-8696 - [c11]Xingjun Ma, Bo Li, Yisen Wang, Sarah M. Erfani, Sudanthi N. R. Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E. Houle, James Bailey:
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality. ICLR 2018 - [c10]Xingjun Ma, Yisen Wang, Michael E. Houle, Shuo Zhou, Sarah M. Erfani, Shu-Tao Xia, Sudanthi N. R. Wijewickrema, James Bailey:
Dimensionality-Driven Learning with Noisy Labels. ICML 2018: 3361-3370 - [c9]Yisen Wang, Bo Dai, Lingkai Kong, Sarah Monazam Erfani, James Bailey, Hongyuan Zha:
Learning Deep Hidden Nonlinear Dynamics from Aggregate Data. UAI 2018: 83-92 - [i7]Xingjun Ma, Bo Li, Yisen Wang, Sarah M. Erfani, Sudanthi N. R. Wijewickrema, Michael E. Houle, Grant Schoenebeck, Dawn Song, James Bailey:
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality. CoRR abs/1801.02613 (2018) - [i6]Yisen Wang, Weiyang Liu, Xingjun Ma, James Bailey, Hongyuan Zha, Le Song, Shu-Tao Xia:
Iterative Learning with Open-set Noisy Labels. CoRR abs/1804.00092 (2018) - [i5]Weiyang Liu, Zhen Liu, Zhiding Yu, Bo Dai, Rongmei Lin, Yisen Wang, James M. Rehg, Le Song:
Decoupled Networks. CoRR abs/1804.08071 (2018) - [i4]Xingjun Ma, Yisen Wang, Michael E. Houle, Shuo Zhou, Sarah M. Erfani, Shu-Tao Xia, Sudanthi N. R. Wijewickrema, James Bailey:
Dimensionality-Driven Learning with Noisy Labels. CoRR abs/1806.02612 (2018) - [i3]Yisen Wang, Bo Dai, Lingkai Kong, Sarah Monazam Erfani, James Bailey, Hongyuan Zha:
Learning Deep Hidden Nonlinear Dynamics from Aggregate Data. CoRR abs/1807.08237 (2018) - 2017
- [j4]Yisen Wang, Fangbing Liu, Shu-Tao Xia, Jia Wu:
Link sign prediction by Variational Bayesian Probabilistic Matrix Factorization with Student-t Prior. Inf. Sci. 405: 175-189 (2017) - [j3]Tao Dai, Zhiya Xu, Haoyi Liang, Ke Gu, Qingtao Tang, Yisen Wang, Weizhi Lu, Shu-Tao Xia:
A generic denoising framework via guided principal component analysis. J. Vis. Commun. Image Represent. 48: 340-352 (2017) - [j2]Yisen Wang, Shu-Tao Xia, Jia Wu:
A less-greedy two-term Tsallis Entropy Information Metric approach for decision tree classification. Knowl. Based Syst. 120: 34-42 (2017) - [c8]Yisen Wang, Simone Romano, Vinh Nguyen, James Bailey, Xingjun Ma, Shu-Tao Xia:
Unbiased Multivariate Correlation Analysis. AAAI 2017: 2754-2760 - [c7]Yisen Wang, Shu-Tao Xia:
Unifying attribute splitting criteria of decision trees by Tsallis entropy. ICASSP 2017: 2507-2511 - [c6]Qingtao Tang, Li Niu, Yisen Wang, Tao Dai, Wangpeng An, Jianfei Cai, Shu-Tao Xia:
Student-t Process Regression with Student-t Likelihood. IJCAI 2017: 2822-2828 - [c5]Qingtao Tang, Tao Dai, Li Niu, Yisen Wang, Shu-Tao Xia, Jianfei Cai:
Robust Survey Aggregation with Student-t Distribution and Sparse Representation. IJCAI 2017: 2829-2835 - [i2]Yisen Wang, Xuejiao Deng, Songbai Pu, Zhiheng Huang:
Residual Convolutional CTC Networks for Automatic Speech Recognition. CoRR abs/1702.07793 (2017) - 2016
- [j1]Yong Cui, Lian Wang, Xin Wang, Yisen Wang, Fengyuan Ren, Shu-Tao Xia:
End-to-end coding for TCP. IEEE Netw. 30(2): 68-73 (2016) - [c4]Qingtao Tang, Yisen Wang, Shu-Tao Xia:
Student-t Process Regression with Dependent Student-t Noise. ECAI 2016: 82-89 - [c3]Yisen Wang, Qingtao Tang, Shu-Tao Xia, Jia Wu, Xingquan Zhu:
Bernoulli Random Forests: Closing the Gap between Theoretical Consistency and Empirical Soundness. IJCAI 2016: 2167-2173 - [c2]Yisen Wang, Shu-Tao Xia:
A novel feature subspace selection method in random forests for high dimensional data. IJCNN 2016: 4383-4389 - [c1]Yisen Wang, Chao-Bing Song, Shu-Tao Xia:
Improving decision trees by Tsallis Entropy Information Metric method. IJCNN 2016: 4729-4734 - 2015
- [i1]Yisen Wang, Chao-Bing Song, Shu-Tao Xia:
Improving Decision Trees Using Tsallis Entropy. CoRR abs/1511.08136 (2015)
Coauthor Index
Sarah M. Erfani
aka: Sarah Monazam Erfani
aka: Sarah Monazam Erfani
[c84] [c83] [c82] [c79] [i84] [i83] [i81] [i80] [i79] [i77] [i76] [i72] [i71] [j9] [c78] [c77] [c74] [c73] [c72] [c71] [c69] [c68] [c67] [c66] [c64] [c63] [c62] [c61] [i67] [i66] [i65] [i64] [i62] [i61] [i60] [i59] [i58] [i57] [i56] [i55] [i54] [i53] [i52] [c60] [c58] [c57] [c54] [c52] [c50] [c49] [c48] [i50] [i49] [i47] [i45] [i44] [i43] [i42] [c37] [c36] [c28] [i38] [i30] [i23] [i20]
Shutao Xia
aka: Shu-Tao Xia
aka: Shu-Tao Xia
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