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

Nalisnick, E., Matsukawa, A., Teh, Y., Gorur, D. and Lakshminarayanan, B. (2019) “Hybrid models with deep and invertible features”, in 36th International Conference on Machine Learning, ICML 2019, pp. 8295–8304.
Schwessinger, R., Gosden, M., Downes, D., Brown, R., Telenius, J., Teh, Y., Lunter, G. and Hughes, J. (2019) “DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning.”
Nalisnick, E., Matsukawa, A., Teh, Y., Gorur, D. and Lakshminarayanan, B. (2019) “Do deep generative models know what they don’t know?”, in 7th International Conference on Learning Representations, ICLR 2019.
Le, T., Kosiorek, A., Siddharth, N., Teh, Y. and Wood, F. (2019) “Revisiting reweighted wake-sleep for models with stochastic control flow”, in 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019.
Webb, S., Pawan Kumar, M., Rainforth, T. and Teh, Y. (2019) “A statistical approach to assessing neural network robustness”, in 7th International Conference on Learning Representations, ICLR 2019.
Maddison, C., Lawson, D., Tucker, G., Heess, N., Doucet, A., Mnih, A. and Teh, Y. (2019) “Particle value functions”, 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. (2019) “Particle value functions”, in 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings.
Mitrovic, J., Sejdinovic, D. and Teh, Y. (2019) “Deep kernel machines via the kernel reparametrization trick”, in 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings.
Kim, H., Mnih, A., Schwarz, J., Garnelo, M., Eslami, A., Rosenbaum, D., Vinyals, O. and Teh, Y. (2019) “Attentive neural processes”, in 7th International Conference on Learning Representations, ICLR 2019.
Maddison, C., Mnih, A. and Teh, Y. (2019) “The concrete distribution: A continuous relaxation of discrete random variables”, in 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings.

Contact Details

Email: yee.teh@stats.ox.ac.uk

Office: 1.16 

Research Groups

Computational Statistics and Machine Learning

Statistical Theory and Methodology