| Active prompting with chain-of-thought for large language models S Diao, P Wang, Y Lin, T Zhang arXiv preprint arXiv:2302.12246, 2023 | 91 | 2023 |
| Bayesian invariant risk minimization Y Lin, H Dong, H Wang, T Zhang CVPR 2022 (Oral), 2022 | 56 | 2022 |
| Black-box prompt learning for pre-trained language models S Diao, Z Huang, R Xu, X Li, Y Lin, X Zhou, T Zhang arXiv preprint arXiv:2201.08531, 2022 | 54 | 2022 |
| Sparse invariant risk minimization X Zhou*, Y Lin*, W Zhang*, T Zhang ICML 2022, 2022 | 53 | 2022 |
| ZIN: When and How to Learn Invariance Without Environment Partition? Y Lin, S Zhu, L Tan, P Cui NeurIPS 2022, 2022 | 50* | 2022 |
| Model agnostic sample reweighting for out-of-distribution learning X Zhou*, Y Lin*, R Pi*, W Zhang, R Xu, P Cui, T Zhang ICML 2022, 2022 | 45* | 2022 |
| Cable sheath loss reduction strategy research based on the coupled line model Y Lin, Z Xu IEEE Transactions on Power Delivery 30 (5), 2303-2311, 2015 | 44 | 2015 |
| Self-guided noise-free data generation for efficient zero-shot learning J Gao, R Pi, L Yong, H Xu, J Ye, Z Wu, W Zhang, X Liang, Z Li, L Kong ICLR 2023, 2023 | 39* | 2023 |
| ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets D Teney, Y Lin, SJ Oh, E Abbasnejad NeurIPS 2023 (Spotlight), 2022 | 28 | 2022 |
| R-Tuning: Teaching Large Language Models to Refuse Unknown Questions H Zhang*, S Diao*, Y Lin*, YR Fung, Q Lian, X Wang, Y Chen, H Ji, ... arXiv preprint arXiv:2311.09677, 2023 | 19 | 2023 |
| Probabilistic bilevel coreset selection X Zhou, R Pi, W Zhang, Y Lin, Z Chen, T Zhang ICML 2022, 2022 | 16 | 2022 |
| Analysis of coupling effect on LCC-MCC hybrid HVDC from parallel AC lines in close proximity Y Lin, Z Xu, L Xiao, Z Zhang, H Xiao 2015 IEEE Power & Energy Society General Meeting, 1-5, 2015 | 15 | 2015 |
| Speciality vs generality: An empirical study on catastrophic forgetting in fine-tuning foundation models Y Lin, L Tan, H Lin, Z Zheng, R Pi, J Zhang, S Diao, H Wang, H Zhao, ... arXiv preprint arXiv:2309.06256, 2023 | 14* | 2023 |
| A Holistic View of Label Noise Transition Matrix in Deep Learning and Beyond Y Lin*, R Pi*, W Zhang, X Xia, J Gao, X Zhou, T Liu, B Han ICLR 2023 (Spotlight), 2022 | 14* | 2022 |
| Particle-based variational inference with preconditioned functional gradient flow H Dong, X Wang, Y Lin, T Zhang ICLR 2023, 2022 | 8 | 2022 |
| Spurious feature diversification improves out-of-distribution generalization Y Lin*, L Tan*, Y Hao*, H Wong, H Dong, W Zhang, Y Yang, T Zhang arXiv preprint arXiv:2309.17230, 2023 | 7 | 2023 |
| Stable learning via sparse variable independence H Yu, P Cui, Y He, Z Shen, Y Lin, R Xu, X Zhang Proceedings of the AAAI Conference on Artificial Intelligence 37 (9), 10998 …, 2023 | 7 | 2023 |
| An empirical study of invariant risk minimization on deep models Y Lin, Q Lian, T Zhang ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning 1 (2), 7, 2021 | 7 | 2021 |
| Provably invariant learning without domain information X Tan*, Y Lin*, S Zhu, C Qu, X Qiu, X Yinghui, P Cui, Y Qi ICML 2022, 2023 | 4 | 2023 |
| Cable sheath loss analysis based on coupled line model Y Lin, F Yang, Z Xu, H Weng 2014 International Conference on Power System Technology, 1665-1671, 2014 | 2 | 2014 |
