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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 Book Chapter Mining Irregular Time Series Data with Noisy Labels: A Risk Estimation ApproachHan, Kun, Koay, Abigail, Ko, Ryan K. L., Chen, Weitong and Xu, Miao (2024). Mining Irregular Time Series Data with Noisy Labels: A Risk Estimation Approach. Lecture Notes in Computer Science. (pp. 293-307) Singapore: Springer Nature Singapore. doi: 10.1007/978-981-96-1242-0_22 |
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2024 Conference Publication Emotionally guided symbolic music generation using diffusion models: the AGE-DM approachZhang, Mingzhe, Ferris, Laura J., Yue, Lin and Xu, Miao (2024). Emotionally guided symbolic music generation using diffusion models: the AGE-DM approach. MMAsia '24, Auckland, New Zealand, 3 - 6 December 2024. New York, NY, United States: ACM. doi: 10.1145/3696409.3700289 |
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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 Conference Publication Irregularity-informed time series analysis: adaptive modelling of spatial and temporal dynamicsZheng, Liangwei Nathan, Li, Zhengyang, Dong, Chang George, Zhang, Wei Emma, Yue, Lin, Xu, Miao, Maennel, Olaf and Chen, Weitong (2024). Irregularity-informed time series analysis: adaptive modelling of spatial and temporal dynamics. 33rd ACM International Conference on Information and Knowledge Management (CIKM), Boise, ID, United States, 21-25 October 2024. New York, United States: Association for Computing Machinery. doi: 10.1145/3627673.3679716 |
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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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2024 Conference Publication Inspecting prediction confidence for detecting black-box backdoor attacks Wang, Tong, Yao, Yuan, Xu, Feng, Xu, Miao, An, Shengwei and Wang, Ting (2024). Inspecting prediction confidence for detecting black-box backdoor attacks . Thirty-Eighth AAAI Conference on Artificial Intelligence, Vancouver, Canada, 20-27 February 2024. Washington, DC, United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v38i1.27780 |
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2024 Conference Publication CaMU: Disentangling Causal Effects in Deep Model UnlearningShen, Shaofei, Zhang, Chenhao, Bialkowski, Alina, Chen, Weitong and Xu, Miao (2024). CaMU: Disentangling Causal Effects in Deep Model Unlearning. 2024 SIAM InternationalConference on Data Mining (SDM'24), Houston, TX United States, 18 - 20 April 2024. Philadelphia, PA United States: Society for Industrial and Applied Mathematics Publications. doi: 10.1137/1.9781611978032.89 |
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2024 Conference Publication Label-agnostic forgetting: a supervision-free unlearning in deep modelsShen, Shaofei, Zhang, Chenhao, Zhao, Yawen, Chen, Weitong, Bialkowski, Alina and Xu, Miao (2024). Label-agnostic forgetting: a supervision-free unlearning in deep models. 12th International Conference on Learning Representations, ICLR 2024, Vienna, Austria, 7-11 May 2024. Vienna, Austria: International Conference on Learning Representations, ICLR. |
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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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2023 Book Chapter Words can be confusing: stereotype bias removal in text classification at the word levelShen, Shaofei, Zhang, Mingzhe, Chen, Weitong, Bialkowski, Alina and Xu, Miao (2023). Words can be confusing: stereotype bias removal in text classification at the word level. Advances in knowledge discovery and data mining. (pp. 99-111) edited by Hisashi Kashima, Tsuyoshi Ide and Wen-Chih Peng. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-33383-5_8 |
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2023 Conference Publication Unbiased risk estimator to multi-labeled complementary label learningGao, Yi, Xu, Miao and Zhang, Min-Ling (2023). Unbiased risk estimator to multi-labeled complementary label learning. 32nd International Joint Conference on Artificial Intelligence (IJCAI), Macao, Peoples Republic of China, 19-25 August 2023. Freiburg, Germany: International Joint Conference on Artificial Intelligence. doi: 10.24963/ijcai.2023/415 |
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2023 Conference Publication Death comes but why: an interpretable illness severity predictions in ICUShen, Shaofei, Xu, Miao, Yue, Lin, Boots, Robert and Chen, Weitong (2023). Death comes but why: an interpretable illness severity predictions in ICU. Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data, Nanjing, China, 11-13 August 2022. Heidelberg, Germany: Springer. doi: 10.1007/978-3-031-25158-0_6 |
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2023 Conference Publication A progressive sampling method for dual -node imbalanced learning with restricted data accessQiu, Yixuan, Chen, Weitong and Xu, Miao (2023). A progressive sampling method for dual -node imbalanced learning with restricted data access. 23rd IEEE International Conference on Data Mining (IEEE ICDM), Shanghai, Peoples R China, 1-4 December 2023. Piscataway, NJ, United States: IEEE Computer Society. doi: 10.1109/ICDM58522.2023.00060 |
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2022 Conference Publication Positive-unlabeled learning using random forests via recursive greedy risk minimizationWilton, Jonathan, Koay, Abigail M. Y., Ko, Ryan K. L., Miao Xu and Ye, Nan (2022). Positive-unlabeled learning using random forests via recursive greedy risk minimization. 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, United States, 29 November - 1 December 2022. New Orleans, LA, United States: Neural information processing systems foundation. |
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2022 Conference Publication Fair Representation Learning: An Alternative to Mutual InformationLiu, Ji, Li, Zenan, Yao, Yuan, Xu, Feng, Ma, Xiaoxing, Xu, Miao and Tong, Hanghang (2022). Fair Representation Learning: An Alternative to Mutual Information. KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC United States, 14 - 18 August 2022. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3534678.3539302 |
