Dr Miao Xu
Dr

Miao Xu

Email: 

Availability

Dr Miao Xu is:
Available for supervision

Qualifications

  • Doctor of Philosophy, Nanjing University

Search Professor Miao Xu’s works on UQ eSpace

52 works between 2013 and 2025
All (52) Journal Article (12) Conference Publication (39) Book Chapter (1)

41 - 52 of 52 works

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.

SIGUA: Forgetting may make learning with noisy labels more robust

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.

Trading personalization for accuracy: data debugging in collaborative filtering

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.

Provably consistent partial-label learning

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.

Progressive identification of true labels for partial-label learning

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

Clipped Matrix Completion: A Remedy for Ceiling Effects

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

Co-teaching: Robust training of deep neural networks with extremely noisy labels

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

Active Feature Acquisition with Supervised Matrix Completion

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

Kernel method for matrix completion with side information and its application in multi-label learning

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

Incomplete Label Distribution Learning

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.

CUR algorithm for partially observed matrices

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.

Multi-label learning with PRO LOSS

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.

Speedup matrix completion with side information: application to multi-label learning

Current funding

  • 2024 - 2026
    Towards knowledge discovery from imperfect and evolving data (ARC Discovery Project administered by The University of Adelaide)
    University of Adelaide
    Open grant
  • 2023 - 2026
    Detecting Key Concepts from Low-Quality Data for Better Decision
    ARC Discovery Early Career Researcher Award
    Open grant

Availability

Dr Miao Xu is:
Available for supervision

Looking for a supervisor? Read our advice on how to choose a supervisor.

Available projects

Supervision history

Current supervision

  • Doctor Philosophy

    Deep learning methods for imbalanced medical multivariate time series data

    Principal Advisor

  • Doctor Philosophy

    Data-limited Class Unlearning: from Methodology to Evaluation

    Principal Advisor

  • Doctor Philosophy

    Weakly Supervised Learning for Mental Health

    Principal Advisor

    Other advisors: Dr Laura Ferris

  • Doctor Philosophy

    High-stakes Decision Making with Weakly Supervised Data

    Principal Advisor

    Other advisors: Dr Nan Ye

  • 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

    Other advisors: Dr Yujun Cai

  • Doctor Philosophy

    Domain Adaptation in Causality Views

    Principal Advisor

    Other advisors: Dr Alina Bialkowski

  • Doctor Philosophy

    Towards knowledge discovery from imperfect and evolving data

    Principal Advisor

    Other advisors: Associate Professor Xin Yu

  • Doctor Philosophy

    Demand Profile Modeling for Low-Voltage Distribution System

    Associate Advisor

    Other advisors: Associate Professor Archie Chapman

  • Doctor Philosophy

    Understanding Human Intention and Performance

    Associate Advisor

    Other advisors: Dr Heming Du, Associate Professor Xin Yu

  • Doctor Philosophy

    System Privacy Compliancy

    Associate Advisor

    Other advisors: Dr Naipeng Dong

  • Doctor Philosophy

    Understanding Human Movements and Sport Performance Analysis

    Associate Advisor

    Other advisors: Associate Professor Xin Yu

  • 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

    Compressed Video Restoration

    Associate Advisor

    Other advisors: Dr Heming Du, Associate Professor Xin Yu

  • Doctor Philosophy

    LLMs for Regulation Compliance

    Associate Advisor

    Other advisors: Dr Naipeng Dong

  • Doctor Philosophy

    Efficient Methods for Automating Reconstruction of Provenance and Cryptocurrency Networks for Crime Attribution

    Associate Advisor

    Other advisors: Professor Ryan Ko

  • Doctor Philosophy

    Integrating Deep Learning and Remote Sensing for Precision Agriculture in Staple Crops

    Associate Advisor

    Other advisors: Associate Professor Xin Yu

  • Doctor Philosophy

    Robust learning algorithms for weakly supervised data

    Associate Advisor

    Other advisors: Dr Nan Ye

  • Doctor Philosophy

    Multimodal foundation model design and analysis

    Associate Advisor

    Other advisors: Associate Professor Xin Yu, Dr Heming Du

Completed supervision

Enquiries

For media enquiries about Dr Miao Xu's areas of expertise, story ideas and help finding experts, contact our Media team:

communications@uq.edu.au