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
Towards better generalization for neural network-based SAT solvers
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
2022
Journal Article
Personalized on-device e-health analytics with decentralized block coordinate descent
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
2022
Conference Publication
What leads to arrhythmia: active causal representation learning of ECG classification
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
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
2022
Conference Publication
A boosting algorithm for training from only unlabeled data
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
2022
Conference Publication
Improving traffic load prediction with multi-modality: a case study of Brisbane
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
2022
Conference Publication
Investigating active positive-unlabeled learning with deep networks
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
2022
Conference Publication
ESTD: Empathy Style Transformer with Discriminative Mechanism
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
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
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
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
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
Funding
Current funding
Supervision
Availability
- Dr Miao Xu is:
- Available for supervision
Before you email them, read our advice on how to contact 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
-
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
High-stakes Decision Making with Weakly Supervised Data
Principal Advisor
Other advisors: Dr Nan Ye
-
Doctor Philosophy
High-stakes Decision Making with Weakly Supervised Data
Principal Advisor
Other advisors: Dr Nan Ye
-
Doctor Philosophy
Fairness-aware Personal Medicine: From Disease Diagnosis to Treatment
Principal Advisor
-
Doctor Philosophy
Deep learning methods for imbalanced medical multivariate time series data
Principal Advisor
-
Doctor Philosophy
Real-time Analytics on Urban Trajectory Data for Road Traffic Management
Principal Advisor
Other advisors: Associate Professor Jiwon Kim
-
Doctor Philosophy
Weakly Supervised Learning for Mental Health
Principal Advisor
Other advisors: Dr Laura Ferris
-
Doctor Philosophy
Machine Learning for Cyber Security
Principal Advisor
Other advisors: Professor Ryan Ko
-
Doctor Philosophy
Deep learning methods for imbalanced medical multivariate time series data
Principal Advisor
-
Doctor Philosophy
Multimodal foundation model design and analysis
Associate Advisor
Other advisors: Dr Xin Yu
-
Doctor Philosophy
Understanding Human Intention and Performance
Associate Advisor
Other advisors: Dr Xin Yu
-
-
Doctor Philosophy
Federated Graph Neural Network-based Recommender Systems
Associate Advisor
Other advisors: Professor Hongzhi Yin
-
Doctor Philosophy
Secure Cross-device Federated Recommender Systems
Associate Advisor
Other advisors: Professor Hongzhi Yin
-
Doctor Philosophy
Efficient Methods for Automating Reconstruction of Provenance and Cryptocurrency Networks for Crime Attribution
Associate Advisor
Other advisors: Professor Ryan Ko
-
Doctor Philosophy
Knowledge Graph-based Conversational Recommender Systems
Associate Advisor
Other advisors: Professor Hongzhi Yin
-
Doctor Philosophy
Demand Profile Modeling for Low-Voltage Distribution System
Associate Advisor
Other advisors: Associate Professor Archie Chapman
-
-
Doctor Philosophy
Secure Cross-device Federated Recommender Systems
Associate Advisor
Other advisors: Professor Hongzhi Yin
-
Doctor Philosophy
Secure Cross-device Federated Recommender Systems
Associate Advisor
Other advisors: Professor Hongzhi Yin
-
Doctor Philosophy
Advancing Human Perception: Countering Evolving Malicious Fake Visual Data
Associate Advisor
Other advisors: Dr Xin Yu
Completed supervision
-
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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