Research Output 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).
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 level. Dive into details by clicking on the dots.
Research Output 2016 2018
Geometric Enclosing Networks
Le, T., Vu, H., Nguyen, T. D. & Phung, D., 2018, Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18). Lang, J. (ed.). California USA: International Joint Conferences on Artificial Intelligence, p. 2355-2361 7 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Open Access
File
GoGP: scalable geometric-based Gaussian process for online regression
Le, T., Nguyen, K., Nguyen, V., Nguyen, T. D. & Phung, D., 1 Jan 2018, (Accepted/In press) In : Knowledge and Information Systems. 30 p.Research output: Contribution to journal › Article › Research › peer-review
MGAN: training generative adversarial nets with multiple generators
Hoang, Q., Nguyen, T. D., Le, T. & Phung, D., 2018, 6th International Conference on Learning Representations, ICLR 2018. Murray, I., Ranzato, MA. & Vinyals, O. (eds.). Amherst MA USA: OpenReview, 24 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Open Access
File
Robust Bayesian kernel machine via Stein variational gradient descent for big data
Nguyen, K., Le, T., Nguyen, T. D., Phung, D. & Webb, G. I., 19 Jul 2018, KDD'18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Lin, C-J. & Xiong, H. (eds.). New York NY USA: Association for Computing Machinery (ACM), p. 2003-2011 9 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Approximation vector machines for large-scale online learning
Le, T., Nguyen, T. D., Nguyen, V. & Phung, D., 1 Nov 2017, In : Journal of Machine Learning Research. 18, p. 1-55 55 p.Research output: Contribution to journal › Article › Research › peer-review
Open Access
File