| Llm lies: Hallucinations are not bugs, but features as adversarial examples JY Yao, KP Ning, ZH Liu, MN Ning, YY Liu, L Yuan arXiv preprint arXiv:2310.01469, 2023 | 193 | 2023 |
| Active Learning for Open-set Annotation KP Ning, X Zhao, Y Li, SJ Huang CVPR 2022, 2022 | 44 | 2022 |
| Asynchronous Active Learning with Distributed Label Querying SJ Huang, CC Zong, KP Ning, HB Ye IJCAI 2021, 2021 | 13 | 2021 |
| Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries KP Ning, L Tao, S Chen, SJ Huang AAAI 2021, 2021 | 13 | 2021 |
| Tackle balancing constraint for incremental semi-supervised support vector learning S Yu, B Gu, K Ning, H Chen, J Pei, H Huang KDD 2019, 2019 | 8 | 2019 |
| Reinforcement learning with supervision from noisy demonstrations KP Ning, SJ Huang arXiv preprint arXiv:2006.07808, 2020 | 7 | 2020 |
| An airport scene delay prediction method based on lstm Z Li, H Chen, J Ge, K Ning Advanced Data Mining and Applications: 14th International Conference, ADMA …, 2018 | 7 | 2018 |
| PiCO: Peer Review in LLMs based on the Consistency Optimization KP Ning, S Yang, Y Liu, JY Yao, Z Liu, T Yonghong, S Yibing, L Yuan ICLR 2025, 2024 | 6* | 2024 |
| Bidirectional Uncertainty-Based Active Learning for Open Set Annotation CC Zong, YW Wang, KP Ning, H Ye, SJ Huang ECCV 2024, 2024 | 3 | 2024 |
| Co-imitation learning without expert demonstration KP Ning, H Xu, K Zhu, SJ Huang ICLR2023 workshop, 2021 | 3 | 2021 |
| Sparse Orthogonal Parameters Tuning for Continual Learning KP Ning, HJ Ke, YY Liu, JY Yao, YH Tian, L Yuan arXiv preprint arXiv:2411.02813, 2024 | 2 | 2024 |
| GPT as a Monte Carlo Language Tree: A Probabilistic Perspective KP Ning, JY Yao, YY Liu, MN Ning, L Yuan arXiv preprint arXiv:2501.07641, 2025 | | 2025 |
| Towards Better Seach Query Classification with Distribution-Diverse Multi-Expert Knowledge Distillation in JD Ads Search KP Ning, M Pang, Z Fang, X Jiang, XW Zhao, CP Peng, ZG Lin, JH Hu, ... CIKM 2024, 2024 | | 2024 |
| Is Parameter Collision Hindering Continual Learning in LLMs? S Yang, KP Ning, YY Liu, JY Yao, YH Tian, YB Song, L Yuan COLING 2025, 2024 | | 2024 |
| Learning a Reusable Meta Denoiser for Learning with Noisy Labels on Multiple Target Domains MK Xie, BS Zou, G Niu, CC Zong, KP Ning, M Sugiyama, SJ Huang | | |
Li Yuan, 袁粒Peking University, Shenzhen Graduate School, School of ECE在 pku.edu.cn 的电子邮件经过验证