| 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 | 407 | 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 | 266 | 2021 |
| Hierarchically Fair Federated Learning J Zhang, C Li, A Robles-Kelly, M Kankanhalli Technical Report, 2020 | 69 | 2020 |
| 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 | 62* | 2021 |
| 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 | 61 | 2022 |
| 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 | 37 | 2019 |
| 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 | 36 | 2021 |
| Robust federated recommendation system C Chen, J Zhang, AKH Tung, M Kankanhalli, G Chen arXiv preprint arXiv:2006.08259, 2020 | 32 | 2020 |
| 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 | 25 | 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 | 20 | 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 | 17* | 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 | 15 | 2022 |
| On the effectiveness of adversarial training against backdoor attacks Y Gao, D Wu, J Zhang, G Gan, ST Xia, G Niu, M Sugiyama IEEE Transactions on Neural Networks and Learning Systems, 2023 | 14 | 2023 |
| 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 | 8 | 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 | 8* | 2022 |
| Autolora: A parameter-free automated robust fine-tuning framework X Xu, J Zhang, M Kankanhalli arXiv preprint arXiv:2310.01818, 2023 | 7 | 2023 |
| 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 36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022), 2022 | 7 | 2022 |
| Where is the Bottleneck of Adversarial Learning with Unlabeled Data? J Zhang, B Han, G Niu, T Liu, M Sugiyama arXiv preprint arXiv:1911.08696, 2019 | 7 | 2019 |
| BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning J Zhang, B Song, H Wang, B Han, T Liu, L Liu, M Sugiyama IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024 | 6 | 2024 |
| Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization X Xu, J Zhang, F Liu, M Sugiyama, M Kankanhalli 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023), 2023 | 5 | 2023 |
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of Tokyo在 k.u-tokyo.ac.jp 的电子邮件经过验证
