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Clustering Effect of (Linearized) Adversarial Robust Models [PDF] [Code]
Yang Bai*, Xin Yan*, Yong Jiang, Shu-Tao Xia#, Yisen Wang#
Neural Information Processing Systems (NeurIPS 2021), 2021 (Spotlight, Top 3%)
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Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State [PDF] [Code]
Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Yisen Wang, Zhouchen Lin
Neural Information Processing Systems (NeurIPS 2021), 2021 (Spotlight, Top 3%)
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Dissecting the Diffusion Process in Linear Graph Convolutional Networks [PDF] [Code]
Yifei Wang, Yisen Wang#, Jiansheng Yang, Zhouchen Lin
Neural Information Processing Systems (NeurIPS 2021), 2021
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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
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Adversarial Neuron Pruning Purifies Backdoored Deep Models [PDF] [Code]
Dongxian Wu, Yisen Wang#
Neural Information Processing Systems (NeurIPS 2021), 2021
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Moire Attack (MA): A New Potential Risk of Screen Photos [PDF] [Code]
Dantong Niu, Guo Ruohao, Yisen Wang#
Neural Information Processing Systems (NeurIPS 2021), 2021
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A Unified Game-Theoretic Interpretation of Adversarial Robustness [PDF] [Code]
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
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Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks [PDF] [Code]
Hanxun Huang, Yisen Wang, Sarah Monazam Erfani, Quanquan Gu, James Bailey, Xingjun Ma
Neural Information Processing Systems (NeurIPS 2021), 2021
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Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks [PDF] [Code]
Chen Ma, Xiangyu Guo, Li Chen, Jun-Hai Yong, Yisen Wang
Neural Information Processing Systems (NeurIPS 2021), 2021
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On Training Implicit Models [PDF]
Zhengyang Geng, Xin-Yu Zhang, Shaojie Bai, Yisen Wang, Zhouchen Lin
Neural Information Processing Systems (NeurIPS 2021), 2021
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Reparameterized Sampling for Generative Adversarial Networks [PDF] [Code] [Award]
Yifei Wang, Yisen Wang#, Jiansheng Yang, Zhouchen Lin
European Conference on Machine Learning (ECML 2021), 2021 (Best (Student) Machine Learning Paper Award)
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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)
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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%)
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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%)
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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
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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
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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%)
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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%)
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A Unified Approach to Interpreting and Boosting Adversarial Transferability [PDF] [Code]
Xin Wang*, Jie Ren*, Shuyun Lin, Xiangming Zhu, Yisen Wang, Quanshi Zhang
International Conference on Learning Representations (ICLR 2021), 2021
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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
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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
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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
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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)
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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
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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
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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
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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)
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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
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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
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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
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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
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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)
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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)
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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)
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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)
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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
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Residual convolutional CTC networks for automatic speech recognition [PDF]
Yisen Wang*, Xuejiao Deng*, Songpai Pu, Zhiheng Huang
Arxiv Technical Report, 2017
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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)
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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.
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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)
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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)
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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)
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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)
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