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

Foster, A., Jankowiak, M., O’Meara, M., Teh, Y. and Rainforth, T. (2020) “A unified stochastic gradient approach to designing Bayesian-optimal experiments”, in Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics. PMLR, pp. 2959–2969.

Bloem-Reddy, B. and Teh, Y. (2020) “Probabilistic symmetries and invariant neural networks”, Journal of Machine Learning Research, 21(90), p. 1−61.

Tomašev, N., Cornebise, J., Hutter, F., Mohamed, S., Picciariello, A., Connelly, B., Belgrave, D., Ezer, D., Haert, F., Mugisha, F., Abila, G., Arai, H., Almiraat, H., Proskurnia, J., Snyder, K., Otake-Matsuura, M., Othman, M., Glasmachers, T., Wever, W., Teh, Y., Khan, M., Winne, R., Schaul, T. and Clopath, C. (2020) “AI for social good: unlocking the opportunity for positive impact”, Nature Communications, 11(1).

Sharma, M., Mindermann, S., Brauner, J., Leech, G., Stephenson, A., Gavenciak, T., Kulveit, J., Teh, Y., Chindelevitch, L. and Gal, Y. (2020) “How robust are the estimated effects of nonpharmaceutical interventions against COVID-19?”, in Advances in Neural Information Processing Systems.

Simsekli, U., Zhu, L., Teh, Y. and Gürbüzbalaban, M. (2020) “Fractional underdamped langevin dynamics: Retargeting SGD with momentum under heavy-tailed gradient noise”, in 37th International Conference on Machine Learning, ICML 2020, pp. 8917–8927.

Xu, J., Ton, J., Kim, H., Kosiorek, A. and Teh, Y. (2020) “MetaFun: Meta-learning with iterative functional updates”, in 37th International Conference on Machine Learning, ICML 2020, pp. 10548–10558.

Jayakumar, S., Czarnecki, W., Menick, J., Schwarz, J., Rae, J., Osidnero, S., Teh, Y., Harley, T. and Pascanu, R. (2020) “MULTIPLICATIVE INTERACTIONS AND WHERE TO FIND THEM”, in 8th International Conference on Learning Representations, ICLR 2020.

Titsias, M., Schwarz, J., de Matthews, A., Pascanu, R. and Teh, Y. (2020) “FUNCTIONAL REGULARISATION FOR CONTINUAL LEARNING WITH GAUSSIAN PROCESSES”, in 8th International Conference on Learning Representations, ICLR 2020.

He, B., Lakshminarayanan, B. and Teh, Y. (2020) “Bayesian deep ensembles via the neural tangent kernel”, in Advances in Neural Information Processing Systems.

Lee, J., Lee, Y., Kim, J., Yang, E., Hwang, S. and Teh, Y. (2020) “Bootstrapping neural processes”, in Advances in Neural Information Processing Systems.

Contact Details

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

Office: 1.16

Research Groups

Computational Statistics and Machine Learning

Statistical Theory and Methodology