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

Teh, Y., Blundell, C. and Elliott, L. (2011) “Modelling genetic variations with fragmentation-coagulation processes”, in Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011.
Görür, D. and Teh, Y. (2011) “Concave-Convex Adaptive Rejection Sampling”, Journal of Computational and Graphical Statistics, 20(3), pp. 670–691.
Rao, V. and Teh, Y. (2011) “Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks”, Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011, pp. 619–626.
Gasthaus, J. and Teh, Y. (2010) “Improvements to the sequence memoizer”, Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010 [Preprint].
Teh, Y. and Jordan, M. (2010) “Hierarchical Bayesian nonparametric models with applications”, in Hjort, N. et al. (eds.) Bayesian Nonparametrics. Cambridge University Press, pp. 158–207.
Gasthaus, J., Wood, F. and Teh, Y. (2010) “Lossless Compression Based on the Sequence Memoizer”, in. Institute of Electrical and Electronics Engineers (IEEE), pp. 337–345.
Blundell, C., Teh, Y. and Heller, K. (2010) “Bayesian rose trees”, Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010, pp. 65–72.
Gasthaus, J. and Teh, Y. (2010) “Improvements to the sequence memoizer”, in Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010.
Heller, K., Teh, Y. and Görür, D. (2009) “Infinite hierarchical hidden Markov models”, Journal of Machine Learning Research, 5, pp. 224–231.
Chieu, H., Lee, W. and Teh, Y. (2009) “Cooled and relaxed survey propagation for MRFs”, Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference [Preprint].

Contact Details

Email: yee.teh@stats.ox.ac.uk

Office: 1.16 

Research Groups

Computational Statistics and Machine Learning

Statistical Theory and Methodology