Projects per year
Personal profile
Biography
My research involves both theoretical and practical aspects. More specifically, this focuses on deep generative models, kernel methods, optimization in machine learning and Bayesian inference whose gained theories can be applied to supervised learning, semi-supervised learning, adversarial learning, online learning, anomaly detection, and cyber security. I have published in the top-notch conferences and high quality journals in machine learning, artificial intelligence, and data mining including NIPS, ICLR, AISTATS, UAI, IJCAI, ICDM and Journal of Machine Learning Research (JMLR).
Education/Academic qualification
Computer Science, Doctor of Philosophy, University of Canberra
Award Date: 27 Mar 2013
Research area keywords
- Deep Generative Models
- Optimisation for Machine Learning
- Kernel Methods
- Online Learning
- Deep Learning for Cyber Security
- Anomaly Detection
Network
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Optimal Transport Theory for Machine Learning with Limited and Less Labels
Asian Office of Aerospace Research And Development (AOARD)
9/09/21 → 8/09/23
Project: Research
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Improving kernel online learning with a snapshot memory
Le, T., Nguyen, K. & Phung, D., Mar 2022, In: Machine Learning. 111, p. 997–1018 22 p.Research output: Contribution to journal › Article › Research › peer-review
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Explain2Attack: text adversarial attacks via cross-domain interpretability
Hossam, M., Le, T., Zhao, H. & Phung, D., 2021, Proceedings of ICPR 2020, 25th International Conference on Pattern Recognition. Boyer, K., C.Lovell, B., Pelillo, M., Sebe, N., Vidal, R. & Yu, J. (eds.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, p. 8922-8928 7 p. (Proceedings - International Conference on Pattern Recognition).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
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Improving ensemble robustness by collaboratively promoting and demoting adversarial robustness
Bui, A. T., Le, T., Zhao, H., Montague, P., de Vel, O., Abraham, T. & Phung, D., 2021, Proceedings of the AAAI Conference on Artificial Intelligence, AAAI-21. Palo Alto CA USA: Association for the Advancement of Artificial Intelligence (AAAI), p. 6831-6839 9 p. (35th AAAI Conference on Artificial Intelligence, AAAI 2021; vol. 8A).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Open AccessFile -
Information-theoretic source code vulnerability highlighting
Nguyen, V., Le, T., De Vel, O., Montague, P., Grundy, J. & Phung, D., 2021, 2021 International Joint Conference on Neural Networks (IJCNN 2021). Hou, Z-G. (ed.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, p. 4823-4830 8 p. (Proceedings of the International Joint Conference on Neural Networks; vol. 2021-July).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
1 Citation (Scopus) -
MOST: Multi-source domain adaptation via optimal transport for student-teacher learning
Nguyen, T., Le, T., Zhao, H., Tran, Q. H., Nguyen, T. & Phung, D., 2021, Proceedings of the thirty-seventh conference on Uncertainty in Artificial Intelligence. Campos, C. & H. Maathuis, M. (eds.). London UK: Proceedings of Machine Learning Research (PMLR), p. 225-235 11 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Open AccessFile4 Citations (Scopus)
Prizes
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The first prize of Imagine Cup 2010 Australia round
Le, Trung (Recipient), 2010
Prize: Prize (including medals and awards)