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

Eric, N., Matsukawa, A., Teh, Y., Gorur, D. and Lakshminarayanan, B. (2019) “Hybrid models with deep and invertible features”, in Proceedings of Machine Learning Research. Proceedings of Machine Learning Research, pp. 4723–4732.

Lee, J., Lee, Y., Kim, J., Kosiorek, A., Choi, S. and Teh, Y. (2019) “Set transformer: A framework for attention-based permutation-invariant neural networks”, in Proceedings of Machine Learning Research. PMLR, pp. 3744–3753.

Gram-Hansen, B., de Witt, C., Rainforth, T., Torr, P., Teh, Y. and Baydin, A. (2019) “Hijacking Malaria Simulators with Probabilistic Programming”, arXiv.

Lee, J., Lee, Y., Kim, J., Kosiorek, A., Choi, S. and Teh, Y. (2019) “Set transformer: A framework for attention-based permutation-invariant neural networks”, in. Proceedings of Machine Learning Research, pp. 3744–3753.

Nalisnick, E., Matsukawa, A., Teh, Y., Gorur, D. and Lakshminarayanan, B. (2019) “Do deep generative models know what they don’t know?”, in International Conference on Learning Representations.

Merel, J., Hasenclever, L., Galashov, A., Ahuja, A., Pham, V., Wayne, G., Teh, Y. and Heess, N. (2019) “Neural probabilistic motor primitives for humanoid control”, in International Conference on Learning Representations.

Galashov, A., Schwarz, J., Kim, H., Garnelo, M., Saxton, D., Kohli, P., Eslami, S. and Teh, Y. (2019) “Meta-Learning surrogate models for sequential decision making.”

Webb, S., Rainforth, T., Teh, Y. and Mudigonda, P. (2019) “A statistical approach to assessing neural network robustness”, in Seventh International Conference on Learning Representations (ICLR 2019). International Conferences on Learning Representations.

Le, T., Kosiorek, A., Siddharth, N., Teh, Y. and Wood, F. (2019) “Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow”, in Proceedings of Machine Learning Research, pp. 1039–1049.

Mathieu, E., Rainforth, T., Siddharth, N. and Teh, Y. (2019) “Disentangling disentanglement in variational autoencoders”, in 36th International Conference on Machine Learning, ICML 2019, pp. 7744–7754.

Contact Details

Email: yee.teh@stats.ox.ac.uk

Office: 1.16

Research Groups

Computational Statistics and Machine Learning

Statistical Theory and Methodology