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

Favaro, S., Lijoi, A., Nava, C., Nipoti, B., Prünster, I. and Teh, Y. (2016) “On the stick-breaking representation for homogeneous NRMIs”, Bayesian Analysis, 11(3), pp. 697–724.
Vollmer, S., Zygalakis, K. and Teh, Y. (2016) “Exploration of the (non-)asymptotic bias and variance of stochastic gradient Langevin dynamics”, Journal of Machine Learning Research, 17(9), pp. 1–33.
James, L., Orbanz, P. and Teh, Y. (2015) “Scaled subordinators and generalizations of the Indian buffet process.”
Moreno, P., Artes-Rodriguez, A., Teh, Y. and Perez-Cruz, F. (2015) “Bayesian Nonparametric Crowdsourcing”, Journal of Machine Learning Research [Preprint].
Favaro, S., Nipoti, B. and Teh, Y. (2015) “Rediscovery of Good–Turing estimators via Bayesian nonparametrics”, Biometrics, 72(1), pp. 136–145.
Lakshminarayanan, B., Roy, D. and Teh, Y. (2015) “Particle Gibbs for Bayesian additive regression trees”, Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS) 2015, San Diego, CA, USA, 38, pp. 553–561.
Adams, R., Fox, E., Sudderth, E. and Teh, Y. (2015) “Guest Editors’ Introduction to the Special Issue on Bayesian Nonparametrics.”, IEEE transactions on pattern analysis and machine intelligence, 37(2), pp. 209–211.
Lienart, T., Teh, Y. and Doucet, A. (2015) “Expectation particle belief propagation”, in Advances in Neural Information Processing Systems, pp. 3609–3617.
De Iorio, M., Favaro, S. and Teh, Y. (2015) “Bayesian inference on population structure: From parametric to nonparametric modeling”, in Nonparametric Bayesian Inference in Biostatistics, pp. 135–152.
Favaro, S., Lomeli, M. and Teh, Y. (2015) “On a class of (Formula presented.) -stable Poisson–Kingman models and an effective marginalized sampler”, Statistics and Computing, 25(1), pp. 67–78.

Contact Details

Email: yee.teh@stats.ox.ac.uk

Office: 1.16 

Research Groups

Computational Statistics and Machine Learning

Statistical Theory and Methodology