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 | 62 | 2022 |
Random Noise Defense Against Query-Based Black-Box Attacks Z Qin, Y Fan, H Zha, B Wu NeurIPS 2021, 2021 | 54 | 2021 |
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 | 24 | 2023 |
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks Z Qin, L Yao, D Chen, Y Li, B Ding, M Cheng KDD 2023, 2023 | 18 | 2023 |
Towards Stable Backdoor Purification through Feature Shift Tuning R Min*, Z Qin*, L Shen, M Cheng NeurIPS 2023, 2023 | 8 | 2023 |
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 | 5 | 2024 |
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 | 4 | 2024 |
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 |