|
2025 Conference Publication Understanding Why Large Language Models Can Be Ineffective in Time Series Analysis: The Impact of Modality AlignmentZheng, Liangwei Nathan, Dong, Chang, Zhang, Wei Emma, Yue, Lin, Xu, Miao, Maennel, Olaf and Chen, Weitong (2025). Understanding Why Large Language Models Can Be Ineffective in Time Series Analysis: The Impact of Modality Alignment. New York, NY, USA: ACM. doi: 10.1145/3711896.3737169 |
|
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 |
|
2024 Conference Publication 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. 35th Australasian Database Conference, ADC 2024, Gold Coast, QLD Australia, 16–18 December 2024. Singapore: Springer. doi: 10.1007/978-981-96-1242-0_22 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
2024 Conference Publication Unlearning From Weakly Supervised LearningTang, Yi, Gao, Yi, Luo, Yong-Gang, Yang, Ju-Cheng, Xu, Miao and Zhang, Min-Ling (2024). Unlearning From Weakly Supervised Learning. 33rd International Joint Conference on Artificial Intelligence (IJCAI), Jeju, South Korea, 3-9 August 2024. Freiburg, Germany: IJCAI. doi: 10.24963/ijcai.2024/553 |
|
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 |
|
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 |
|
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. |
|
2024 Conference Publication Machine Unlearning: Challenges in Data Quality and AccessXu, Miao (2024). Machine Unlearning: Challenges in Data Quality and Access. 33rd International Joint Conference on Artificial Intelligence (IJCAI), Jeju, South Korea, 3-9 August 2024. Freiburg, Germany: IJCAI. doi: 10.24963/ijcai.2024/987 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
