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., Kurihara, K. and Welling, M. (2009) “Collapsed variational inference for HDP”, Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference [Preprint].

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].

Wood, F. and Teh, Y. (2009) “A hierarchical nonparametric Bayesian approach to statistical language model domain adaptation”, Journal of Machine Learning Research, 5, pp. 607–614.

Wood, F. et al. (2009) “A stochastic memoizer for sequence data”, pp. 1129–1136.

Doshi, F. et al. (2009) “Variational Inference for the Indian Buffet Process”, in van Dyk, D. and Welling, M. (eds.), pp. 137–144.

Roy, D. and Teh, Y. (2009) “The Mondrian process”, Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference, pp. 1377–1384.

Van Gael, J., Teh, Y. and Ghahramani, Z. (2009) “The infinite factorial hidden Markov model”, Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference, pp. 1697–1704.

Quon, G. et al. (2009) “A mixture model for the evolution of gene expression in non-homogeneous datasets”, Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference, pp. 1297–1304.

Haffari, G. and Teh, Y. (2009) “Hierarchical dirichlet trees for information retrieval”, pp. 173 – 173.

Rao, V. and Teh, Y. (2009) “Spatial normalized gamma processes”, Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference, pp. 1554–1562.

Contact Details

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

Office: 1.16

Research Groups

Computational Statistics and Machine Learning

Statistical Theory and Methodology