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

Tirumala, D., Galashov, A., Noh, H., Hasenclever, L., Pascanu, R., Schwarz, J., Desjardins, G., Czarnecki, W., Ahuja, A., Teh, Y. and Heess, N. (2022) “Behavior Priors for Efficient Reinforcement Learning”, Journal of Machine Learning Research, 23.
Naderiparizi, S., Scibior, A., Munk, A., Ghadiri, M., Baydin, A., Gram-Hansen, B., Schroeder, C., Zinkov, R., Torr, P., Rainforth, T., Teh, Y. and Wood, F. (2022) “Amortized rejection sampling in universal probabilistic programming”, in Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022). Journal of Machine Learning Research, pp. 8392–8412.
Dupont, E., Teh, Y. and Doucet, A. (2022) “Generative models as distributions of functions”, in. Journal of Machine Learning Research, pp. 2989–3015.
Nicholson, G., Blangiardo, M., Briers, M., Diggle, P., Fjelde, T., Ge, H., Goudie, R., Jersakova, R., King, R., Lehmann, B., Mallon, A.-M., Padellini, T., Teh, Y., Holmes, C. and Richardson, S. (2022) “Interoperability of statistical models in pandemic preparedness: principles and reality.”, Statistical science : a review journal of the Institute of Mathematical Statistics, 37(2), pp. 183–206.
Taufiq, M., Ton, J., Cornish, R., Teh, Y. and Doucet, A. (2022) “Conformal Off-Policy Prediction in Contextual Bandits”, in Advances in Neural Information Processing Systems.
Miao, N., Mathieu, E., Siddharth, N., Teh, Y. and Rainforth, T. (2022) “ON INCORPORATING INDUCTIVE BIASES INTO VAES”, in ICLR 2022 - 10th International Conference on Learning Representations.
Rudner, T., Chen, Z., Teh, Y. and Gal, Y. (2022) “Tractable Function-Space Variational Inference in Bayesian Neural Networks”, in Advances in Neural Information Processing Systems.
Rudner, T., Smith, F., Feng, Q., Teh, Y. and Gal, Y. (2022) “Continual Learning via Sequential Function-Space Variational Inference”, in Proceedings of Machine Learning Research, pp. 18871–18887.
Di Benedetto, G., Caron, F. and Teh, Y. (2021) “Non-exchangeable random partition models for microclustering”, Annals of Statistics, 49(4), pp. 1931–1957.
Chau, S., Ton, J.-F., González, J., Teh, Y. and Sejdinovic, D. (2021) “BayesIMP: Uncertainty Quantification for Causal Data Fusion”, arXiv.

Contact Details

Email: yee.teh@stats.ox.ac.uk

Office: 1.16 

Research Groups

Computational Statistics and Machine Learning

Statistical Theory and Methodology