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

Lu, X., Rainforth, T. and Teh, Y. (2023) “Daisee: Adaptive importance sampling by balancing exploration and exploitation”, Scandinavian Journal of Statistics, 50(3), pp. 1298–1324.
Klarner, L., Rudner, T., Reutlinger, M., Schindler, T., Morris, G., Deane, C. and Teh, Y. (2023) “Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions”, arXiv.
De Bortoli, V., Mathieu, E., Hutchinson, M., Thornton, J., Teh, Y. and Doucet, A. (2023) “Riemannian score-based generative modelling”, in Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Curran Associates, pp. 2406–2422.
Naik, C., Caron, F., Rousseau, J., Teh, Y. and Palla, K. (2023) “Bayesian nonparametrics for sparse dynamic networks”, in Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Springer Nature, pp. 191–206.
Naik, C., Caron, F., Rousseau, J., Teh, Y. and Palla, K. (2023) “Bayesian Nonparametrics for Sparse Dynamic Networks”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Nature, pp. 191–206.
Miao, N., Rainforth, T., Mathieu, E., Dubois, Y., Teh, Y., Foster, A. and Kim, H. (2023) “Learning Instance-Specific Augmentations by Capturing Local Invariances”, in Proceedings of Machine Learning Research, pp. 24720–24736.
Schwarz, J., Tack, J., Teh, Y., Lee, J. and Shin, J. (2023) “Modality-Agnostic Variational Compression of Implicit Neural Representations”, in Proceedings of Machine Learning Research, pp. 30342–30364.
Zaidi, S., Berariu, T., Kim, H., Bornschein, J., Clopath, C., Teh, Y. and Pascanu, R. (2023) “When Does Re-initialization Work?”, in Proceedings of Machine Learning Research, pp. 12–26.
Dupont, E., Loya, H., Alizadeh, M., Golinski, A., Teh, Y. and Doucet, A. (2022) “COIN++: neural compression across modalities”, Transactions on Machine Learning Research, 2022(11).
Teh, Y., Elesedy, B., He, B., Hutchinson, M., Zaidi, S., Bhoopchand, A., Paquet, U., Tomasev, N., Read, J. and Diggle, P. (2022) “Authors’ reply to the discussion of ‘Efficient Bayesian Inference of Instantaneous Reproduction Numbers at Fine Spatial Scales, with an Application to Mapping and Nowcasting the Covid-19 Epidemic in British Local Authorities’ by Teh et al. in Session 2 of the Royal Statistical Society’s Special Topic Meeting on COVID-19 transmission: 11 June 2021”, Journal of the Royal Statistical Society. Series A: Statistics in Society, 185(S1), pp. S107 - S109.

Contact Details

Email: yee.teh@stats.ox.ac.uk

Office: 1.16 

Research Groups

Computational Statistics and Machine Learning

Statistical Theory and Methodology