Dr Miao Xu
ARC DECRA
School of Electrical Engineering and Computer Science
Affiliate of Centre for Enterprise AI
Centre for Enterprise AI
Book Chapters
Han, 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
Shen, 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
Journal Articles
Han, 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
Gao, 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
Zhao, 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
Hu, 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
Zhang, 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
Hu, 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
Qiu, 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
Lv, 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
Ye, 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
Xu, 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
Xu, 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
Xu, 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
Conference Papers
Zhang, 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
Zheng, 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
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
Shen, 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
Shen, 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.
Gao, 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
Qiu, 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
Shen, 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
Wilton, 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.
Liu, 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
Zhang, Chenhao, Zhang, Yanjun, Mao, Jeff, Chen, Weitong, Yue, Lin, Bai, Guangdong and Xu, Miao (2022). Towards better generalization for neural network-based SAT solvers. 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, 16-19 May 2022. CHAM: Springer Science and Business Media Deutschland GmbH. doi: 10.1007/978-3-031-05936-0_16
Zhao, Yawen, Yue, Lin and Xu, Miao (2022). A boosting algorithm for training from only unlabeled data. 18th International Conference on Advanced Data Mining and Applications, ADMA 2022, Brisbane, QLD, Australia, 28-30 November 2022. Heidelberg, Germany: Springer. doi: 10.1007/978-3-031-22137-8_34
Zhang, Mingzhe, Yue, Lin and Xu, Miao (2022). ESTD: Empathy Style Transformer with Discriminative Mechanism. 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, 28-30 November 2022. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-22137-8_5
Shen, Shaofei, Chen, Weitong and Xu, Miao (2022). What leads to arrhythmia: active causal representation learning of ECG classification. 35th Australasian Joint Conference, AI 2022, Perth, WA, Australia, 5-8 December 2022. Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-031-22695-3_35
Tran, Khai Phan, Chen, Weitong and Xu, Miao (2022). Improving traffic load prediction with multi-modality: a case study of Brisbane. 34th Australasian Joint Conference, AI 2021, Sydney, NSW, Australia, 2-4 February 2022. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-030-97546-3_21
Han, Kun, Chen, Weitong and Xu, Miao (2022). Investigating active positive-unlabeled learning with deep networks. Australasian Joint Conference on Artificial Intelligence (AI), Electr Network, 2-4 February 2022. Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-030-97546-3_49
Qiu, Yixuan, Chen, Weitong, Yue, Lin, Xu, Miao and Zhu, Baofeng (2022). STCT: Spatial-temporal conv-transformer network for cardiac arrhythmias recognition. International Conference on Advanced Data Mining and Applications, Sydney, NSW, Australia, 2-4 February 2022. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-95405-5_7
Wang, Yanda, Chen, Weitong, Pi, Dechang, Yue, Lin, Xu, Miao and Li, Xue (2021). Multi-hop reading on memory neural network with selective coverage for medication recommendation. ACM International Conference on Information & Knowledge Management, Virtual Event, 1-5 November 2021. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3459637.3482278
Wang, Yanda, Chen, Weitong, PI, Dechang, Yue, Lin, Wang, Sen and Xu, Miao (2021). Self-supervised adversarial distribution regularization for medication recommendation. Thirtieth International Joint Conference on Artificial Intelligence, Montreal, Canada, 19-27 August 2021. California, United States: International Joint Conferences on Artificial Intelligence Organization. doi: 10.24963/ijcai.2021/431
Su, Guangxin, Chen, Weitong and Xu, Miao (2021). Positive-unlabeled learning from imbalanced data. Thirtieth International Joint Conference on Artificial Intelligence, Montreal, Canada, 19-27 August 2021. California, United States: International Joint Conferences on Artificial Intelligence Organization. doi: 10.24963/ijcai.2021/412
Feng, Lei, Shu, Senlin, Lu, Nan, Han, Bo, Xu, Miao, Niu, Gang, An, Bo and Sugiyama, Masashi (2021). Pointwise binary classification with pairwise confidence comparisons. International Conference on Machine Learning (ICML), Virtual, 18-24 July, 2021. San Diego, CA, United States: JMLR.
Han, Bo, Niu, Gang, Yu, Xingrui, Yao, Quanming, Xu, Miao, Tsang, Ivor W. and Sugiyama, Masashi (2020). SIGUA: Forgetting may make learning with noisy labels more robust. 37th International Conference on Machine Learning, ICML 2020, Virtual, 13-18 July, 2020. San Diego, CA, United States: JMLR.
Chen, Long, Yao, Yuan, Xu, Feng, Xu, Miao and Tong, Hanghang (2020). Trading personalization for accuracy: data debugging in collaborative filtering. Conference on Neural Information Processing Systems, Vancouver, Canada, 6-12 December 2020. Maryland Heights, MO, United States: Morgan Kaufmann Publishers.
Feng, Lei, Lv, Jiaqi, Han, Bo, Xu, Miao, Niu, Gang, Geng, Xin, An, Bo and Sugiyama, Masashi (2020). Provably consistent partial-label learning. Conference on Neural Information Processing Systems, Vancouver, Canada, 6-12 December 2020. Maryland Heights, MO, United States: Morgan Kaufmann Publishers.
Lvy, Jiaqi, Xu, Miao, Feng, Lei, Niu, Gang, Geng, Xin and Sugiyama, Masashi (2020). Progressive identification of true labels for partial-label learning. 37th International Conference on Machine Learning (ICML 2020), Vienna, Austria, 12-18 July 2020. International Machine Learning Society.
Teshima, Takeshi, Xu, Miao, Sato, Issei and Sugiyama, Masashi (2019). Clipped Matrix Completion: A Remedy for Ceiling Effects. Thirty-Third AAAI Conference on Artificial Intelligence, Honolulu, HI United States, 27 January – 1 February 2019. PALO ALTO: Association for the Advancement of Artificial Intelligence (AAAI). doi: 10.1609/aaai.v33i01.33015151
Han, Bo, Yao, Quanming, Yu, Xingrui, Niu, Gang, Xu, Miao, Hu, Weihua, Tsang, Ivor W. and Sugiyama, Masashi (2018). Co-teaching: Robust training of deep neural networks with extremely noisy labels. 32nd Conference on Neural Information Processing Systems (NIPS), Montreal, Canada, 2-8 December, 2018. Maryland Heights, MO, United States: Morgan Kaufmann Publishers. doi: 10.5555/3327757.3327944
Huang, Sheng-Jun, Xu, Miao, Xie, Ming-Kun, Sugiyama, Masashi, Niu, Gang and Chen, Songcan (2018). Active Feature Acquisition with Supervised Matrix Completion. 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, United Kingdom, July 2018. New York, NY United States: ACM. doi: 10.1145/3219819.3220084
Xu, Miao and Zhou, Zhi-Hua (2017). Incomplete Label Distribution Learning. Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, VIC Australia, 19-25 August 2017. Melbourne, VIC Australia: International Joint Conferences on Artificial Intelligence Organization. doi: 10.24963/ijcai.2017/443
Xu, Miao, Jin, Rong and Zhou, Zhi-Hua (2015). CUR algorithm for partially observed matrices. 32nd International Conference on Machine Learning, Lille, France, 7-9 July, 2015. San Diego, CA, United States: JMLR.
Xu, Miao, Li, Yu-Feng and Zhou, Zhi-Hua (2013). Multi-label learning with PRO LOSS. AAAI-13: Twenty-Seventh Conference on Artificial Intelligence, Bellevue, WA USA, 14-18 July 2013.
Xu, Miao, Jin, Rong and Zhou, Zhi-Hua (2013). Speedup matrix completion with side information: application to multi-label learning. NIPS'13: Proceedings of the 26th International Conference on Neural Information Processing Systems, Lake Tahoe, NV USA, 5-10 December 2013. Maryland Heights, MO USA: Morgan Kaufmann Publishers.