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

Patterson, S. and Teh, Y. (2013) “Stochastic gradient Riemannian Langevin dynamics on the probability simplex”, in Advances in Neural Information Processing Systems.
Chen, C. et al. (2013) “Dependent normalized random measures”, in 30th International Conference on Machine Learning, ICML 2013, pp. 2006–2014.
Caron, F. and Teh, Y. (2012) “Bayesian nonparametric models for ranked data”, in Advances in Neural Information Processing Systems, pp. 1520–1528.
Mnih, A. and Teh, Y. (2012) “Learning label trees for probabilistic modelling of implicit feedback”, Advances in Neural Information Processing Systems, 4, pp. 2816–2824.
Alexe, B. et al. (2012) “Searching for objects driven by context”, Advances in Neural Information Processing Systems, 2, pp. 881–889.
Rao, V. and Teh, Y. (2012) “MCMC for continuous-time discrete-state systems”, Advances in Neural Information Processing Systems, 1, pp. 701–709.
Elliott, L. and Teh, Y. (2012) “Scalable imputation of genetic data with a discrete fragmentation- coagulation process”, Advances in Neural Information Processing Systems, 4, pp. 2852–2860.
Caron, F., Teh, Y. and Murphy, T. (2012) “Bayesian nonparametric Plackett-Luce models for the analysis of clustered ranked data.”
Mnih, A. and Teh, Y. (2012) “A fast and simple algorithm for training neural probabilistic language models”, Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 2, pp. 1751–1758.
Teh, Y. (2011) “Bayesian tools for natural language learning invited talk”, CoNLL 2011 - Fifteenth Conference on Computational Natural Language Learning, Proceedings of the Conference, p. 219.

Contact Details

Email: yee.teh@stats.ox.ac.uk

Office: 1.16 

Research Groups

Computational Statistics and Machine Learning

Statistical Theory and Methodology