Overview
Availability
- Dr Miao Xu is:
- Available for supervision
Qualifications
- Doctor of Philosophy, Nanjing University
Works
Search Professor Miao Xu’s works on UQ eSpace
2022
Conference Publication
STCT: Spatial-temporal conv-transformer network for cardiac arrhythmias recognition
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
2021
Conference Publication
Multi-hop reading on memory neural network with selective coverage for medication recommendation
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
2021
Conference Publication
Self-supervised adversarial distribution regularization for medication recommendation
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
2021
Conference Publication
Positive-unlabeled learning from imbalanced data
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
2021
Conference Publication
Pointwise binary classification with pairwise confidence comparisons
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.
2021
Journal Article
Learning from group supervision: the impact of supervision deficiency on multi-label learning
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
2020
Journal Article
Robust multi-label learning with PRO Loss
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
2020
Conference Publication
SIGUA: Forgetting may make learning with noisy labels more robust
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.
2020
Conference Publication
Provably consistent partial-label learning
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.
2020
Conference Publication
Progressive identification of true labels for partial-label learning
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.
2020
Conference Publication
Trading personalization for accuracy: data debugging in collaborative filtering
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.
2019
Conference Publication
Clipped Matrix Completion: A Remedy for Ceiling Effects
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
2018
Conference Publication
Co-teaching: Robust training of deep neural networks with extremely noisy labels
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
2018
Conference Publication
Active Feature Acquisition with Supervised Matrix Completion
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
2017
Journal Article
Kernel method for matrix completion with side information and its application in multi-label learning
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
2017
Conference Publication
Incomplete Label Distribution Learning
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
2015
Conference Publication
CUR algorithm for partially observed matrices
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.
2013
Conference Publication
Multi-label learning with PRO LOSS
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.
2013
Conference Publication
Speedup matrix completion with side information: application to multi-label learning
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.
Funding
Current funding
Supervision
Availability
- Dr Miao Xu is:
- Available for supervision
Looking for a supervisor? Read our advice on how to choose a supervisor.
Available projects
-
Detecting Key Concepts from Low-Quality Data for Better Decision
Recruiting students with strong academic background and interest in weakly supervised machine learning.
Supervision history
Current supervision
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Doctor Philosophy
Weakly Supervised Learning for Mental Health
Principal Advisor
-
Doctor Philosophy
Data-limited Class Unlearning: from Methodology to Evaluation
Principal Advisor
-
Doctor Philosophy
Towards knowledge discovery from imperfect and evolving data
Principal Advisor
Other advisors: Associate Professor Xin Yu
-
Doctor Philosophy
Deep learning methods for imbalanced medical multivariate time series data
Principal Advisor
-
Doctor Philosophy
Trustworthy Learning from Irregular Time Series: Robust Modeling and Interpretable Decision-Making
Principal Advisor
Other advisors: Professor Ryan Ko
-
Doctor Philosophy
Multi-Modal Perception for Context-Aware Systems
Principal Advisor
-
Doctor Philosophy
Domain Adaptation in Causality Views
Principal Advisor
Other advisors: Dr Alina Bialkowski
-
Doctor Philosophy
High-stakes Decision Making with Weakly Supervised Data
Principal Advisor
Other advisors: Dr Nan Ye
-
Doctor Philosophy
Analysis of Machine Learning Systems
Associate Advisor
Other advisors: Dr Naipeng Dong
-
Doctor Philosophy
Combating evolving deceptive fake visual information through deepfake detection
Associate Advisor
Other advisors: Associate Professor Xin Yu
-
-
Doctor Philosophy
Integrating Deep Learning and Remote Sensing for Precision Agriculture in Staple Crops
Associate Advisor
Other advisors: Associate Professor Xin Yu
-
Doctor Philosophy
Multimodal foundation model design and analysis
Associate Advisor
Other advisors: Associate Professor Xin Yu, Dr Heming Du
-
Doctor Philosophy
Understanding Human Movements and Sport Performance Analysis
Associate Advisor
Other advisors: Associate Professor Xin Yu
-
Doctor Philosophy
Efficient Methods for Automating Reconstruction of Provenance and Cryptocurrency Networks for Crime Attribution
Associate Advisor
Other advisors: Professor Ryan Ko
-
Doctor Philosophy
Compressed Video Restoration
Associate Advisor
Other advisors: Dr Heming Du, Associate Professor Xin Yu
-
Doctor Philosophy
Demand Profile Modeling for Low-Voltage Distribution System
Associate Advisor
Other advisors: Associate Professor Archie Chapman
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-
Doctor Philosophy
Robust learning algorithms for weakly supervised data
Associate Advisor
Other advisors: Dr Nan Ye
-
Doctor Philosophy
Understanding Human Intention and Performance
Associate Advisor
Other advisors: Dr Heming Du, Associate Professor Xin Yu
Completed supervision
-
2026
Doctor Philosophy
A Study on Map-Matching on Wireless Traffic Sensor Data
Principal Advisor
Other advisors: Associate Professor Jiwon Kim
-
2025
Doctor Philosophy
Secure Cross-device Federated Recommender Systems
Associate Advisor
Other advisors: Professor Hongzhi Yin
-
2024
Doctor Philosophy
Federated Graph Neural Network-based Recommender Systems
Associate Advisor
Other advisors: Professor Hongzhi Yin
-
2023
Doctor Philosophy
From Cloud to Device: Transforming Recommender Systems for On-Device Deployment
Associate Advisor
Other advisors: Professor Hongzhi Yin
-
2023
Doctor Philosophy
Decentralized On-device Machine Learning and Unlearning for IoT Collaboration
Associate Advisor
Other advisors: Professor Hongzhi Yin
Media
Enquiries
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