| 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 | 429 | 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 | 280 | 2021 |
| Hierarchically Fair Federated Learning J Zhang, C Li, A Robles-Kelly, M Kankanhalli Technical Report, 2020 | 77 | 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 | 64* | 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 | 62 | 2022 |
| 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 | 39 | 2021 |
| 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 | 38 | 2019 |
| Robust federated recommendation system C Chen, J Zhang, AKH Tung, M Kankanhalli, G Chen arXiv preprint arXiv:2006.08259, 2020 | 33 | 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 | 27 | 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 | 21 | 2021 |
| 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 | 19 | 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 | 16 | 2023 |
| 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 20 (3), 1882-1894, 2022 | 11 | 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 | 10 | 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 | 10* | 2022 |
| Guided interpolation for adversarial training C Chen, J Zhang, X Xu, T Hu, G Niu, G Chen, M Sugiyama arXiv preprint arXiv:2102.07327, 2021 | 10 | 2021 |
| 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 | 9 | 2024 |
| Autolora: A parameter-free automated robust fine-tuning framework X Xu, J Zhang, M Kankanhalli arXiv preprint arXiv:2310.01818, 2023 | 9 | 2023 |
| An LLM can Fool Itself: A Prompt-Based Adversarial Attack X Xu, K Kong, N Liu, L Cui, D Wang, J Zhang, M Kankanhalli arXiv preprint arXiv:2310.13345, 2023 | 8 | 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 | 8 | 2022 |
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp