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

Fernandez, T., Rivera, N. and Teh, Y. (2016) “Gaussian processes for survival analysis”, in Advances in Neural Information Processing Systems 29: 30th Annual Conference on Neural Information Processing Systems 2016. Curran Associates, pp. 5021–5029.
Fernández, T. and Teh, Y. (2016) “Posterior Consistency for a Non-parametric Survival Model under a Gaussian Process Prior.”
Elliott, L. and Teh, Y. (2016) “A nonparametric HMM for genetic imputation and coalescent inference”, Electronic Journal of Statistics, 10(2), pp. 3425–3451.
Balog, M., Lakshminarayanan, B., Ghahramani, Z., Roy, D. and Teh, Y. (2016) “The Mondrian kernel”, in 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016). Association for Uncertainty in Artificial Intelligence Press, pp. 32–41.
Lomelí, M., Favaro, S. and Teh, Y. (2016) “A marginal sampler for σ-stable Poisson–Kingman mixture models”, Journal of Computational and Graphical Statistics, 26(1), pp. 44–53.
Mitrovic, J., Sejdinovic, D. and Teh, Y.-W. (2016) “DR-ABC: Approximate Bayesian computation with kernel-based distribution regression”, in ICML 2016: 33rd International Conference on Machine Learning. Journal of Machine Learning Research.
Kim, H., Lu, X., Flaxman, S. and Teh, Y. (2016) “Collaborative Filtering with Side Information: a Gaussian Process Perspective.”
Lakshminarayanan, B., Roy, D. and Teh, Y. (2016) “Mondrian Forests for Large-Scale Regression when Uncertainty Matters”, in Proceedings of Machine Learning Research. 19th International Conference on Artificial Intelligence and Statistics, pp. 1478–1487.
Teh, Y., Thiery, A. and Vollmer, S. (2016) “Consistency and fluctuations for stochastic gradient Langevin dynamics”, Journal of Machine Learning Research, 17(7), pp. 1–33.
Teh, Y. (2016) “Bayesian nonparametric modeling and the ubiquitous ewens sampling formula”, Statistical Science, 31(1), pp. 34–36.

Contact Details

Email: yee.teh@stats.ox.ac.uk

Office: 1.16 

Research Groups

Computational Statistics and Machine Learning

Statistical Theory and Methodology