| NGC: A Unified Framework for Learning with Open-World Noisy Data ZF Wu, T Wei, J Jiang, C Mao, M Tang, YF Li IEEE/CVF International Conference on Computer Vision (ICCV), 2021 | 98 | 2021 |
| Does Tail Label Help for Large-Scale Multi-Label Learning T Wei, YF Li International Joint Conference on Artificial Intelligence, 2847-2853, 2018 | 63 | 2018 |
| Robust long-tailed learning under label noise T Wei, JX Shi, WW Tu, YF Li arXiv preprint arXiv:2108.11569, 2021 | 55 | 2021 |
| Learning safe multi-label prediction for weakly labeled data T Wei, LZ Guo, YF Li, W Gao Machine Learning 107, 703-725, 2018 | 40 | 2018 |
| Towards automated semi-supervised learning YF Li, H Wang, T Wei, WW Tu Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 4237-4244, 2019 | 39 | 2019 |
| Towards realistic long-tailed semi-supervised learning: Consistency is all you need T Wei, K Gan Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 33 | 2023 |
| Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts JX Shi, T Wei, Z Zhou, JJ Shao, XY Han, YF Li Forty-first International Conference on Machine Learning, 2024 | 28* | 2024 |
| Learning for Tail Label Data: A Label-Specific Feature Approach. T Wei, WW Tu, YF Li IJCAI, 3842-3848, 2019 | 22 | 2019 |
| How re-sampling helps for long-tail learning? JX Shi, T Wei, Y Xiang, YF Li Advances in Neural Information Processing Systems 36, 2023 | 21 | 2023 |
| Towards robust prediction on tail labels T Wei, WW Tu, YF Li, GP Yang Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 20 | 2021 |
| Prototypical classifier for robust class-imbalanced learning T Wei, JX Shi, YF Li, ML Zhang Pacific-Asia Conference on Knowledge Discovery and Data Mining, 44-57, 2022 | 19 | 2022 |
| Transfer and share: semi-supervised learning from long-tailed data T Wei, QY Liu, JX Shi, WW Tu, LZ Guo Machine Learning 113 (4), 1725-1742, 2024 | 12 | 2024 |
| Learning compact model for large-scale multi-label data T Wei, YF Li Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 5385-5392, 2019 | 12 | 2019 |
| A survey on extreme multi-label learning T Wei, Z Mao, JX Shi, YF Li, ML Zhang arXiv preprint arXiv:2210.03968, 2022 | 11 | 2022 |
| Probabilistic label tree for streaming multi-label learning T Wei, JX Shi, YF Li Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 11 | 2021 |
| MixPUL: consistency-based augmentation for positive and unlabeled learning T Wei, F Shi, H Wang, WWTYF Li arXiv preprint arXiv:2004.09388, 2020 | 11 | 2020 |
| Robust model selection for positive and unlabeled learning with constraints T Wei, H Wang, W Tu, Y Li Science China Information Sciences 65 (11), 212101, 2022 | 10 | 2022 |
| EAT: Towards Long-Tailed Out-of-Distribution Detection T Wei, BL Wang, ML Zhang Proceedings of the AAAI Conference on Artificial Intelligence 38 (14), 15787 …, 2024 | 7 | 2024 |
| Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning K Gan, T Wei arXiv preprint arXiv:2405.11756, 2024 | 6 | 2024 |
| Residual diverse ensemble for long-tailed multi-label text classification J Shi, T Wei, Y Li Science China Information Sciences 67 (11), 212102, 2024 | 5 | 2024 |
