Mr Meng Fang

  • Room: Level: 08 Room: 8.11
  • Building: Doug McDonell Building
  • Campus: Parkville

Research interests

  • Active learning, Transfer learning, Multi-armed bandits


Meng Fang received his B.Eng. and M.Eng. at Wuhan University and a Ph.D in computer science at University of Technology, Sydney. His research focuses on using machine learning techniques to solve real-world problems.

Recent publications

  1. Fang M, Yin J, Zhu X. Active exploration for large graphs. DATA MINING AND KNOWLEDGE DISCOVERY. Kluwer Academic Publishers. 2016, Vol. 30, Issue 3.
  2. Fang M, Yin J, Hall LO, Tao D. Active Multitask Learning With Trace Norm Regularization Based on Excess Risk. IEEE Transactions on Cybernetics. Institute of Electrical and Electronics Engineers. 2016.
  3. Wang Y, Wenjie Z, Wu L, Lin X, Fang M, Pan S. Iterative views agreement: An iterative low-rank based structured optimization method to multi-view spectral clustering. IJCAI International Joint Conference on Artificial Intelligence. AAAI Press. 2016, Vol. 2016-January.
  4. Fang M, Cohn T. Learning when to trust distant supervision: An application to low-resource POS tagging using cross-lingual projection. 20th Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics. 2016.
  5. Yu B, Fang M, Tao D. Linear submodular bandits with a knapsack constraint. 30th AAAI Conference on Artificial Intelligence, AAAI 2016. 2016.
  6. Wang Z, Du B, Zhang L, Zhang L, Fang M, Tao D. Multi-label Active Learning Based on Maximum Correntropy Criterion: Towards Robust and Discriminative Labeling. 14th European Conference on Computer Vision (ECCV). Springer Verlag. 2016, Vol. 9907. Editors: Leibe B, Matas J, Sebe N, Welling M.
  7. Yin J, Fang M, Mokhtari G, Zhang Q. Multi-resident Location Tracking in Smart Home through Non-wearable Unobtrusive Sensors. 14th International Conference on Smart Homes and Health Telematics (ICOST). Springer Verlag. 2016, Vol. 9677. Editors: Chang CK, Chiari L, Cao Y, Jin H, Mokhtari M, Aloulou H.
  8. Yu B, Fang M, Tao D. Per-Round Knapsack-Constrained Linear Submodular Bandits. NEURAL COMPUTATION. MIT Press. 2016, Vol. 28, Issue 12.
  9. Yu B, Fang M, Tao D, Yin J. Submodular asymmetric feature selection in cascade object detection. 30th AAAI Conference on Artificial Intelligence, AAAI 2016. 2016.
  10. Fang M, Yin J, Zhu X. Supervised sampling for networked data. SIGNAL PROCESSING. Elsevier Science. 2016, Vol. 124.
  11. Fang M, Yin J, Zhu X, Zhang C. TrGraph: Cross-Network Transfer Learning via Common Signature Subgraphs. 2016 32ND IEEE INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE). IEEE. 2016.
  12. Fang M, Tao D. Active multi-task learning via bandits. SIAM International Conference on Data Mining 2015, SDM 2015. 2015.
  13. Gong C, Tao D, Liu W, Maybank SJ, Fang M, Fu K, Yang J. Saliency propagation from simple to difficult. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2015, Vol. 07-12-June-2015.
  14. Fang M, Yin J, Zhu X, Zhang C. TrGraph: Cross-Network Transfer Learning via Common Signature Subgraphs. IEEE Transactions on Knowledge and Data Engineering. IEEE Computer Society. 2015, Vol. 27, Issue 9.
  15. Fang M, Yin J, Tao D. Active learning for crowdsourcing using knowledge transfer. Proceedings of the National Conference on Artificial Intelligence. 2014, Vol. 3.

View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile