Articles with public access mandates - Jingfeng ZhangLearn more
Not available anywhere: 1
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
Mandates: National Natural Science Foundation of China
Available somewhere: 12
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
Mandates: National Natural Science Foundation of China, Research Grants Council, Hong …
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
Mandates: Australian Research Council, National Natural Science Foundation of China
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
Mandates: National Research Foundation, Singapore
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
Mandates: Australian Research Council, National Natural Science Foundation of China
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
Mandates: National Natural Science Foundation of China
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
Mandates: National Natural Science Foundation of China, Japan Science and Technology …
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
Mandates: Australian Research Council, National Natural Science Foundation of China
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
Mandates: US National Science Foundation, Australian Research Council, National …
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
Mandates: US National Science Foundation, Australian Research Council, National …
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks
S Ghamizi, J Zhang, M Cordy, M Papadakis, M Sugiyama, Y Le-Traon
International Conference on Machine Learning (ICML 2023), 2023
Mandates: Luxembourg National Research Fund, Japan Science and Technology Agency
Synergy-of-Experts: Collaborate to Improve Adversarial Robustness
S Cui, J Zhang, J Liang, B Han, M Sugiyama, C Zhang
36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022), 2022
Mandates: National Natural Science Foundation of China
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
Mandates: National Research Foundation, Singapore
Publication and funding information is determined automatically by a computer program