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

Mathieu, E., Foster, A. and Teh, Y. (2021) “On Contrastive Representations of Stochastic Processes”, in Advances in Neural Information Processing Systems, pp. 28823–28835.

Zaidi, S., Zela, A., Elsken, T., Holmes, C., Hutter, F. and Teh, Y. (2021) “Neural Ensemble Search for Uncertainty Estimation and Dataset Shift”, in Advances in Neural Information Processing Systems, pp. 7898–7911.

Hutchinson, M., Le Lan, C., Zaidi, S., Dupont, E., Teh, Y. and Kim, H. (2021) “LieTransformer: Equivariant Self-Attention for Lie Groups”, in Proceedings of Machine Learning Research, pp. 4533–4543.

Holderrieth, P., Hutchinson, M. and Teh, Y. (2021) “Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes”, in Proceedings of Machine Learning Research, pp. 4297–4307.

Di Benedetto, G., Caron, F. and Teh, Y. (2020) “Non-exchangeable random partition models for microclustering”. University of Oxford.

Schwessinger, R., Gosden, M., Downes, D., Brown, R., Oudelaar, A., Telenius, J., Teh, Y., Lunter, G. and Hughes, J. (2020) “DeepC: Predicting 3D genome folding using megabase-scale transfer learning”, Nature Methods, 17(11), pp. 1118–1124.

Wang, Q., Rao, V. and Teh, Y. (2020) “An exact auxiliary variable Gibbs sampler for a class of diffusions”, Journal of Computational and Graphical Statistics, 30(2), pp. 297–311.

Xu, J., Ton, J.-F., Kim, H., Kosiorek, A. and Teh, Y. (2020) “MetaFun: meta-learning with iterative functional updates”, Proceedings of Machine Learning Research, 119, pp. 10617–10627.

Zhou, Y., Yang, H., Teh, Y. and Rainforth, T. (2020) “Divide, conquer, and combine: a new inference strategy for probabilistic programs with stochastic support”, in ICML 2020. ICML Proceedings.

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.

Contact Details

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

Office: 1.16

Research Groups

Computational Statistics and Machine Learning

Statistical Theory and Methodology