| Attacks Which Do Not Kill Training Make Adversarial Learning Stronger J Zhang, X Xu, B Han, G Niu, L Cui, M Sugiyama, M Kankanhalli International Conference on Machine Learning (ICML 2020), 2020 | 234 | 2020 |
| Geometry-aware Instance-reweighted Adversarial Training J Zhang, J Zhu, G Niu, B Han, M Sugiyama, M Kankanhalli International Conference on Learning Representations (ICLR 2021), 2021 | 155 | 2021 |
| Maximum Mean Discrepancy Test is Aware of Adversarial Attacks R Gao, F Liu, J Zhang, B Han, T Liu, G Niu, M Sugiyama International Conference on Machine Learning (ICML 2021), 2021 | 42* | 2021 |
| Hierarchically Fair Federated Learning J Zhang, C Li, A Robles-Kelly, M Kankanhalli Technical Report, 2020 | 33 | 2020 |
| Towards Robust Resnet: A Small Step but A Giant Leap J Zhang, B Han, L Wynter, KH Low, M Kankanhalli International Joint Conference on Artificial Intelligence (IJCAI 2019), 2019 | 30 | 2019 |
| Reliable Adversarial Distillation with Unreliable Teachers J Zhu, J Yao, B Han, J Zhang, T Liu, G Niu, J Zhou, J Xu, H Yang International Conference on Learning Representations (ICLR 2022), 2022 | 16 | 2022 |
| Understanding the interaction of adversarial training with noisy labels J Zhu, J Zhang, B Han, T Liu, G Niu, H Yang, M Kankanhalli, M Sugiyama arXiv preprint arXiv:2102.03482, 2021 | 15 | 2021 |
| Learning Diverse-structured Networks for Adversarial Robustness X Du, J Zhang, B Han, T Liu, Y Rong, G Niu, J Huang, M Sugiyama International Conference on Machine Learning (ICML 2021), 2021 | 15 | 2021 |
| CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection H Yan, J Zhang, G Niu, J Feng, V Tan, M Sugiyama International Conference on Machine Learning (ICML 2021), 2021 | 14 | 2021 |
| Decision Boundary-aware Data Augmentation for Adversarial Training C Chen, J Zhang, X Xu, L Lyu, C Chen, T Hu, G Chen IEEE Transactions on Dependable and Secure Computing (TDSC 2022), 2022 | 9* | 2022 |
| NoiLin: Improving Adversarial Training and Correcting Stereotype of Noisy Labels J Zhang, X Xu, B Han, T Liu, L Cui, G Niu, M Sugiyama Transactions on Machine Learning Research (TMLR 2022), 2022 | 5* | 2022 |
| Towards Adversarially Robust Deep Image Denoising H Yan, J Zhang, J Feng, M Sugiyama, VYF Tan International Joint Conference on Artificial Intelligence (IJCAI 2022), 2022 | 4 | 2022 |
| Wavefuzz: A clean-label poisoning attack to protect your voice Y Ge, Q Wang, J Zhang, J Zhou, Y Zhang, C Shen arXiv preprint arXiv:2203.13497, 2022 | 2 | 2022 |
| On the effectiveness of adversarial training against backdoor attacks Y Gao, D Wu, J Zhang, G Gan, ST Xia, G Niu, M Sugiyama arXiv preprint arXiv:2202.10627, 2022 | 2 | 2022 |
| Adversarial Attack and Defense for Non-Parametric Two-Sample Tests X Xu, J Zhang, F Liu, M Sugiyama, M Kankanhalli International Conference on Machine Learning (ICML 2022), 2022 | 1 | 2022 |
| Synergy-of-Experts: Collaborate to Improve Adversarial Robustness S Cui, J Zhang, J Liang, B Han, M Sugiyama, C Zhang The 36th Annual Conference on Neural Information Processing Systems (NeurIPS …, 2022 | | 2022 |
| Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks J Zhou, J Zhu, J Zhang, T Liu, G Niu, B Han, M Sugiyama The 36th Annual Conference on Neural Information Processing Systems (NeurIPS …, 2022 | | 2022 |
| Bilateral Dependency Optimization: Defending Against Model-inversion Attacks X Peng, F Liu, J Zhang, L Lan, J Ye, T Liu, B Han ACM SIGKDD International Conference on Knowledge Discovery and Data Mining …, 2022 | | 2022 |
| Pre-training Robust Feature Extractor Against Clean-label Data Poisoning Attacks T Zhou, H Yan, L Liu, J Zhang, B Han NeurIPS ML Safety Workshop, 0 | | |
| Model and Method: Training-Time Attack for Cooperative Multi-Agent Reinforcement Learning S Wu, T Wang, X Wu, J Zhang, Y Hu, C Fan, C Zhang Deep Reinforcement Learning Workshop NeurIPS 2022, 0 | | |
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
