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Zeyu Qin
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Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation
Z Qin, Y Fan, Y Liu, L Shen, Y Zhang, J Wang, B Wu
NeurIPS 2022, 2022
622022
Random Noise Defense Against Query-Based Black-Box Attacks
Z Qin, Y Fan, H Zha, B Wu
NeurIPS 2021, 2021
542021
Beyond factuality: A comprehensive evaluation of large language models as knowledge generators
L Chen, Y Deng, Y Bian, Z Qin, B Wu, TS Chua, KF Wong
EMNLP 2023, 2023
242023
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks
Z Qin, L Yao, D Chen, Y Li, B Ding, M Cheng
KDD 2023, 2023
182023
Towards Stable Backdoor Purification through Feature Shift Tuning
R Min*, Z Qin*, L Shen, M Cheng
NeurIPS 2023, 2023
82023
Improving Adversarial Training for Multiple Perturbations through the Lens of Uniform Stability
J Xiao, Z Qin, Y Fan, B Wu, J Wang, ZQ Luo
ICML 2023, The Workshop on New Frontiers in Adversarial Machine Learning, 2023
7*2023
Imitation learning from imperfection: Theoretical justifications and algorithms
Z Li, T Xu, Z Qin, Y Yu, ZQ Luo
NeurIPS 2023, 2024
52024
Step-on-feet tuning: Scaling self-alignment of llms via bootstrapping
H Wang, G Ma, Z Meng, Z Qin, L Shen, Z Zhang, B Wu, L Liu, Y Bian, T Xu, ...
arXiv preprint arXiv:2402.07610, 2024
42024
Entropic Distribution Matching in Supervised Fine-tuning of LLMs: Less Overfitting and Better Diversity
Z Li, C Chen, T Xu, Z Qin, J Xiao, R Sun, ZQ Luo
arXiv preprint arXiv:2408.16673, 2024
2024
MoFO: Momentum-Filtered Optimizer for Mitigating Forgetting in LLM Fine-Tuning
Y Chen, S Wang, Z Lin, Z Qin, Y Zhang, T Ding, R Sun
arXiv preprint arXiv:2407.20999, 2024
2024
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