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Xilie Xu
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Year
Attacks which do not kill training make adversarial learning stronger
J Zhang, X Xu, B Han, G Niu, L Cui, M Sugiyama, M Kankanhalli
ICML 2020, 2020
3822020
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, 2022
15*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, 2022
7*2022
Autolora: A parameter-free automated robust fine-tuning framework
X Xu, J Zhang, M Kankanhalli
ICLR 2024, 2024
62024
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection
X Xu, J Zhang, F Liu, M Sugiyama, M Kankanhalli
NeurIPS 2023 (spotlight), 2023
52023
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization
X Xu, J Zhang, F Liu, M Sugiyama, M Kankanhalli
NeurIPS 2023, 2023
42023
An LLM can Fool Itself: A Prompt-Based Adversarial Attack
X Xu, K Kong, N Liu, L Cui, D Wang, J Zhang, M Kankanhalli
ICLR 2024, 2024
22024
Adversarial Attack and Defense for Non-Parametric Two-Sample Tests
X Xu, J Zhang, F Liu, M Sugiyama, M Kankanhalli
ICML 2022, 2022
12022
Privacy-Preserving Low-Rank Adaptation for Latent Diffusion Models
Z Luo, X Xu, F Liu, YS Koh, D Wang, J Zhang
arXiv preprint arXiv:2402.11989, 2024
2024
Towards Robust Foundation Models: Adversarial Contrastive Learning
J Zhang, X Xu
The Third Blogpost Track at ICLR 2024, 2024
2024
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Articles 1–10