| Multi-instance multi-label learning ZH Zhou, ML Zhang, SJ Huang, YF Li Artificial Intelligence, 2012 | 1047* | 2012 |
| Taking human out of learning applications: A survey on automated machine learning Q Yao, M Wang, Y Chen, W Dai, YF Li, WW Tu, Q Yang, Y Yu arXiv preprint arXiv:1810.13306 31, 2018 | 645 | 2018 |
| Multi-instance learning by treating instances as non-iid samples ZH Zhou, YY Sun, YF Li Proceedings of the 26th annual international conference on machine learning …, 2009 | 632 | 2009 |
| Nyström method vs random fourier features: A theoretical and empirical comparison T Yang, YF Li, M Mahdavi, R Jin, ZH Zhou Advances in neural information processing systems 25, 2012 | 470 | 2012 |
| Towards making unlabeled data never hurt YF Li, ZH Zhou Proceedings of the twenty eighth international conference on machine …, 2011 | 462 | 2011 |
| Dash: Semi-supervised learning with dynamic thresholding Y Xu, L Shang, J Ye, Q Qian, YF Li, B Sun, H Li, R Jin International conference on machine learning, 11525-11536, 2021 | 371 | 2021 |
| Safe deep semi-supervised learning for unseen-class unlabeled data LZ Guo, ZY Zhang, Y Jiang, YF Li, ZH Zhou International conference on machine learning, 3897-3906, 2020 | 294 | 2020 |
| Towards safe weakly supervised learning YF Li, LZ Guo, ZH Zhou IEEE transactions on pattern analysis and machine intelligence 43 (1), 334-346, 2021 | 203 | 2021 |
| Usb: A unified semi-supervised learning benchmark for classification Y Wang, H Chen, Y Fan, W Sun, R Tao, W Hou, R Wang, L Yang, Z Zhou, ... Advances in Neural Information Processing Systems 35, 3938-3961, 2022 | 197 | 2022 |
| Class-imbalanced semi-supervised learning with adaptive thresholding LZ Guo, YF Li International conference on machine learning, 8082-8094, 2022 | 165 | 2022 |
| Safe semi-supervised learning: a brief introduction YF Li, DM Liang Frontiers of Computer Science 13 (4), 669-676, 2019 | 158 | 2019 |
| Tighter and convex maximum margin clustering YF Li, IW Tsang, J Kwok, ZH Zhou Artificial Intelligence and Statistics, 344-351, 2009 | 152 | 2009 |
| Semi-supervised learning using label mean YF Li, JT Kwok, ZH Zhou Proceedings of the 26th Annual International Conference on Machine Learning …, 2009 | 144 | 2009 |
| Convex and scalable weakly labeled SVMs YF Li, IW Tsang, JT Kwok, ZH Zhou The Journal of Machine Learning Research 14 (1), 2151-2188, 2013 | 133 | 2013 |
| Ngc: A unified framework for learning with open-world noisy data ZF Wu, T Wei, J Jiang, C Mao, M Tang, YF Li Proceedings of the IEEE/CVF International Conference on Computer Vision, 62-71, 2021 | 127 | 2021 |
| Cost-sensitive semi-supervised support vector machine YF Li, JTY Kwok, ZH Zhou Proceedings of the National Conference on Artificial Intelligence 1, 500, 2010 | 119 | 2010 |
| A convex method for locating regions of interest with multi-instance learning YF Li, JT Kwok, IW Tsang, ZH Zhou Joint European Conference on Machine Learning and Knowledge Discovery in …, 2009 | 118 | 2009 |
| Learning safe prediction for semi-supervised regression YF Li, HW Zha, ZH Zhou Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017 | 86 | 2017 |
| Diversity regularized machine Y Yu, YF Li, ZH Zhou IJCAI Proceedings-International Joint Conference on Artificial Intelligence …, 2011 | 86 | 2011 |
| Improving Semi-Supervised Support Vector Machines Through Unlabeled Instances Selection YF Li, ZH Zhou Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011 | 84 | 2011 |
Lan-Zhe GuoLAMDA Group, Nanjing UniversityVerified email at lamda.nju.edu.cn
