Professor Yee Whye Teh

Professor of Statistical Machine Learning

Biographical Sketch

Prior to joining Oxford, I was a Lecturer then Reader of Computational Statistics and Machine Learning at the Gatsby Neuroscience Unit, UCL from 2007 to 2012. I obtained my PhD in Computer Science at the University of Toronto in 2003. This was followed by two years as a postdoctoral fellow at University of California, Berkeley, then as Lee Kuan Yew Postdoctoral Fellow at the National University of Singapore.

Research Interests

My research interests lie in the general areas of machine learning, Bayesian statistics and computational statistics. Although my group works on a variety of topics ranging from theoretical, through to methodological and applications, I am personally particularly interested in three (overlapping) themes: Bayesian nonparametrics and probabilistic learning, large scale machine learning, and deep learning.

These themes are motivated by the phenomenal growth in the quantity, diversity and heterogeneity of data now available. The analysis of such data is crucial to opening doors to new scientific frontiers and future economic growth. In the longer term, the development of general methods that can deal with such data are important testing grounds for artificial general intelligence systems.

Publications

Maddison, C., Mnih, A. and Teh, Y. (2017) “The concrete distribution: A continuous relaxation of discrete random variables”, in International Conference on Learning Representations (2017). International Conference on Learning Representations, pp. 1–20.
Lu, X., Perrone, V., Hasenclever, L., Teh, Y. and Vollmer, S. (2017) “Relativistic Monte Carlo”, Proceedings of Machine Learning Research: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 54, pp. 1236–1245.
Flaxman, S., Teh, Y. and Sejdinovic, D. (2017) “Poisson intensity estimation with reproducing kernels”, in International Conference on Artificial Intelligence and Statistics (AISTATS). AI & Statistics.
Arbel, J., Favaro, S., Nipoti, B. and Teh, Y. (2017) “Bayesian nonparametric inference for discovery probabilities: credible intervals and large sample asymptotics”, Statistica Sinica, 27, pp. 839–858.
Mitrovic, J., Sejdinovic, D. and Teh, Y. (2017) “Deep kernel machines via the kernel reparametrization trick”, in 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings.
Maddison, C., Lawson, D., Tucker, G., Heess, N., Doucet, A., Mnih, A. and Teh, Y. (2017) “Particle value functions”, in 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings.
Lu, X., Perrone, V., Hasenclever, L., Teh, Y. and Vollmer, S. (2017) “Relativistic Monte Carlo”, in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017.
Teh, Y., Bapst, V., Czarnecki, W., Quan, J., Kirkpatrick, J., Hadsell, R., Heess, N. and Pascanu, R. (2017) “Distral: robust multitask reinforcement learning”, in Advances in Neural Information Processing Systems 30 (NIPS 2017). Massachusetts Institute of Technology Press, pp. 4497–4507.
Maddison, C., Mnih, A. and Teh, Y. (2017) “The concrete distribution: A continuous relaxation of discrete random variables”, in 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings.
Battiston, M., Favaro, S. and Teh, Y. (2016) “Multi-armed bandit for species discovery: a Bayesian nonparametric approach”, Journal of the American Statistical Association, 113(521), pp. 455–466.

Contact Details

Email: yee.teh@stats.ox.ac.uk

Office: 1.16 

Research Groups

Computational Statistics and Machine Learning

Statistical Theory and Methodology