Prati
Jingfeng Zhang
Jingfeng Zhang
Lecturer@University of Auckland & Scientist@RIKEN AIP
Potvrđena adresa e-pošte na riken.jp - Početna stranica
Naslov
Citirano
Citirano
Godina
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
3682020
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
2322021
Hierarchically Fair Federated Learning
J Zhang, C Li, A Robles-Kelly, M Kankanhalli
Technical Report, 2020
642020
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
57*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
472022
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
382019
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
322021
Robust federated recommendation system
C Chen, J Zhang, AKH Tung, M Kankanhalli, G Chen
arXiv preprint arXiv:2006.08259, 2020
282020
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
232021
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
192021
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
14*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
102023
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
92022
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
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
72019
Autolora: A parameter-free automated robust fine-tuning framework
X Xu, J Zhang, M Kankanhalli
arXiv preprint arXiv:2310.01818, 2023
62023
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
62022
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection
X Xu, J Zhang, F Liu, M Sugiyama, M Kankanhalli
37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023), 2023
42023
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
42022
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
32023
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