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2020 – today
- 2024
[c6]Xilie Xu, Keyi Kong, Ning Liu, Lizhen Cui, Di Wang, Jingfeng Zhang, Mohan S. Kankanhalli:
An LLM can Fool Itself: A Prompt-Based Adversarial Attack. ICLR 2024- [c5]Xilie Xu, Jingfeng Zhang, Mohan S. Kankanhalli:
AutoLoRa: An Automated Robust Fine-Tuning Framework. ICLR 2024 - [i9]Zihao Luo, Xilie Xu, Feng Liu, Yun Sing Koh, Di Wang, Jingfeng Zhang:
Privacy-Preserving Low-Rank Adaptation for Latent Diffusion Models. CoRR abs/2402.11989 (2024) - 2023
- [j2]Chen Chen
, Jingfeng Zhang, Xilie Xu, Lingjuan Lyu, Chaochao Chen, Tianlei Hu, Gang Chen:
Decision Boundary-Aware Data Augmentation for Adversarial Training. IEEE Trans. Dependable Secur. Comput. 20(3): 1882-1894 (2023) - [c4]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization. NeurIPS 2023 - [c3]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection. NeurIPS 2023 - [i8]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection. CoRR abs/2302.03857 (2023) - [i7]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization. CoRR abs/2305.00374 (2023) - [i6]Xilie Xu, Jingfeng Zhang, Mohan S. Kankanhalli:
AutoLoRa: A Parameter-Free Automated Robust Fine-Tuning Framework. CoRR abs/2310.01818 (2023) - [i5]Xilie Xu, Keyi Kong, Ning Liu, Lizhen Cui, Di Wang, Jingfeng Zhang, Mohan S. Kankanhalli:
An LLM can Fool Itself: A Prompt-Based Adversarial Attack. CoRR abs/2310.13345 (2023) - 2022
- [j1]Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Lizhen Cui, Gang Niu, Masashi Sugiyama:
NoiLin: Improving adversarial training and correcting stereotype of noisy labels. Trans. Mach. Learn. Res. 2022 (2022) - [c2]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Adversarial Attack and Defense for Non-Parametric Two-Sample Tests. ICML 2022: 24743-24769 - [i4]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Adversarial Attacks and Defense for Non-Parametric Two-Sample Tests. CoRR abs/2202.03077 (2022) - 2021
- [i3]Chen Chen, Jingfeng Zhang, Xilie Xu, Tianlei Hu, Gang Niu, Gang Chen, Masashi Sugiyama:
Guided Interpolation for Adversarial Training. CoRR abs/2102.07327 (2021) - [i2]Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Gang Niu, Lizhen Cui, Masashi Sugiyama:
NoiLIn: Do Noisy Labels Always Hurt Adversarial Training? CoRR abs/2105.14676 (2021) - 2020
- [c1]Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan S. Kankanhalli:
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. ICML 2020: 11278-11287 - [i1]Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan S. Kankanhalli:
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. CoRR abs/2002.11242 (2020)
Coauthor Index
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