Следене
Jingfeng Zhang
Jingfeng Zhang
Lecturer@University of Auckland & Scientist@RIKEN AIP
Потвърден имейл адрес: riken.jp - Начална страница
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Позовавания
Година
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
4692020
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
3062021
Hierarchically Fair Federated Learning
J Zhang, C Li, A Robles-Kelly, M Kankanhalli
Technical Report, 2020
792020
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
742022
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
71*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
432021
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
412019
Robust federated recommendation system
C Chen, J Zhang, AKH Tung, M Kankanhalli, G Chen
arXiv preprint arXiv:2006.08259, 2020
342020
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
312022
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
292021
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
232023
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
222021
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
152023
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
142022
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
132022
Fair text-to-image diffusion via fair mapping
J Li, L Hu, J Zhang, T Zheng, H Zhang, D Wang
arXiv preprint arXiv:2311.17695, 2023
112023
Autolora: A parameter-free automated robust fine-tuning framework
X Xu, J Zhang, M Kankanhalli
arXiv preprint arXiv:2310.01818, 2023
112023
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
112023
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
11*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
112021
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Статии 1–20