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2025 Journal Article Adapting to the stream: an instance-attention GNN method for irregular multivariate time series dataHan, Kun, Koay, Abigail M. Y., Ko, Ryan K. L., Chen, Weitong and Xu, Miao (2025). Adapting to the stream: an instance-attention GNN method for irregular multivariate time series data. Frontiers of Computer Science, 19 (8) 198340. doi: 10.1007/s11704-024-40449-z |
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2024 Journal Article Complementary to multiple labels: a correlation-aware correction approachGao, Yi, Xu, Miao and Zhang, Min-Ling (2024). Complementary to multiple labels: a correlation-aware correction approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46 (12), 9179-9191. doi: 10.1109/tpami.2024.3416384 |
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2024 Journal Article A boosting framework for positive-unlabeled learningZhao, Yawen, Zhang, Mingzhe, Zhang, Chenhao, Chen, Weitong, Ye, Nan and Xu, Miao (2024). A boosting framework for positive-unlabeled learning. Statistics and Computing, 35 (1) 2. doi: 10.1007/s11222-024-10529-y |
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2024 Journal Article Designing unique and high-performance Al alloys via machine learning: mitigating data bias through active learningHu, Mingwei, Tan, Qiyang, Knibbe, Ruth, Xu, Miao, Liang, Guofang, Zhou, Jianxin, Xu, Jun, Jiang, Bin, Li, Xue, Ramajayam, Mahendra, Dorin, Thomas and Zhang, Ming-Xing (2024). Designing unique and high-performance Al alloys via machine learning: mitigating data bias through active learning. Computational Materials Science, 244 113204, 113204. doi: 10.1016/j.commatsci.2024.113204 |
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2024 Journal Article Mitigating the Impact of Inaccurate Feedback in Dynamic Learning-to-Rank: A Study of Overlooked Interesting ItemsZhang, Chenhao, Chen, Weitong, Zhang, Wei Emma and Xu, Miao (2024). Mitigating the Impact of Inaccurate Feedback in Dynamic Learning-to-Rank: A Study of Overlooked Interesting Items. ACM Transactions on Intelligent Systems and Technology, 16 (1) 5, 1-26. doi: 10.1145/3653983 |
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2023 Journal Article Recent applications of machine learning in alloy design: a reviewHu, Mingwei, Tan, Qiyang, Knibbe, Ruth, Xu, Miao, Jiang, Bin, Wang, Sen, Li, Xue and Zhang, Ming-Xing (2023). Recent applications of machine learning in alloy design: a review. Materials Science and Engineering: R: Reports, 155 100746, 100746. doi: 10.1016/j.mser.2023.100746 |
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2023 Journal Article Pre-training in medical data: a surveyQiu, Yixuan, Lin, Feng, Chen, Weitong and Xu, Miao (2023). Pre-training in medical data: a survey. Machine Intelligence Research, 20 (2), 147-179. doi: 10.1007/s11633-022-1382-8 |
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2023 Journal Article On the robustness of average losses for partial-label learningLv, Jiaqi, Liu, Biao, Feng, Lei, Xu, Ning, Xu, Miao, An, Bo, Niu, Gang, Geng, Xin and Sugiyama, Masashi (2023). On the robustness of average losses for partial-label learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46 (5) 10122995, 1-15. doi: 10.1109/TPAMI.2023.3275249 |
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2022 Journal Article Personalized on-device e-health analytics with decentralized block coordinate descentYe, Guanhua, Yin, Hongzhi, Chen, Tong, Xu, Miao, Nguyen, Quoc Viet Hung and Song, Jiangning (2022). Personalized on-device e-health analytics with decentralized block coordinate descent. IEEE Journal of Biomedical and Health Informatics, 26 (6), 1-1. doi: 10.1109/JBHI.2022.3140455 |
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2021 Journal Article Learning from group supervision: the impact of supervision deficiency on multi-label learningXu, Miao and Guo, Lan-Zhe (2021). Learning from group supervision: the impact of supervision deficiency on multi-label learning. Science China Information Sciences, 64 (3) 130101. doi: 10.1007/s11432-020-3132-4 |
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2020 Journal Article Robust multi-label learning with PRO LossXu, Miao, Li, Yu-Feng and Zhou, Zhi-Hua (2020). Robust multi-label learning with PRO Loss. IEEE Transactions on Knowledge and Data Engineering, 32 (8) 8680669, 1610-1624. doi: 10.1109/tkde.2019.2908898 |
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2017 Journal Article Kernel method for matrix completion with side information and its application in multi-label learningXu, Miao and Zhou, Zhi-Hua (2017). Kernel method for matrix completion with side information and its application in multi-label learning. Scientia Sinica Informationis, 48 (1), 47-59. doi: 10.1360/n112016-00279 |
