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

Kim, H., Mnih, A., Schwarz, J., Garnelo, M., Eslami, A., Rosenbaum, D., Vinyals, O. and Teh, Y. (2019) “Attentive neural processes”, in 7th International Conference on Learning Representations, ICLR 2019.
Mitrovic, J., Sejdinovic, D. and Teh, Y. (2018) “Causal inference via Kernel deviance measures”, in Advances in Neural Information Processing Systems. Massachusetts Institute of Technology Press.
Chen, J., Zhu, J., Teh, Y. and Zhang, T. (2018) “Stochastic expectation maximization with variance reduction”, in Neural Information Processing Systems. Massachusetts Institute of Technology Press.
Miscouridou, X., Caron, F. and Teh, Y. (2018) “Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data”, in Advances in Neural Information Processing Systems 31 (NIPS 2018) pre-proceedings. Neural Information Processing Systems Foundation.
Galashov, A., Jayakumar, S., Hasenclever, L., Tirumala, D., Schwarz, J., Desjardins, G., Czarnecki, W., Teh, Y., Pascanu, R. and Heess, N. (2018) “Information asymmetry in KL-regularized RL.”
Bloem-Reddy, B., Foster, A., Mathieu, E. and Teh, Y. (2018) “Sampling and inference for beta neutral-to-the-left models of sparse networks”, in Uncertainty in Artificial Intelligence: Proceedings of the Thirty-Fourth Conference (2018). AUAI Press, pp. 477–486.
Xu, J. and Teh, Y. (2018) “Controllable Semantic Image Inpainting.”
Czarnecki, W., Jayakumar, S., Jaderberg, M., Hasenclever, L., Teh, Y., Osindero, S., Heess, N. and Pascanu, R. (2018) “Mix&Match - Agent Curricula for Reinforcement Learning.”
Battiston, M., Favaro, S., Roy, D. and Teh, Y. (2018) “A characterization of product-form exchangeable feature probability functions”, Annals of Applied Probability, 28(3), pp. 1423–1448.
Elliott, L., De Iorio, M., Favaro, S., Adhikari, K. and Teh, Y. (2018) “Modeling population structure under hierarchical Dirichlet processes”, Bayesian Analysis, 14(2), pp. 313–339.

Contact Details

Email: yee.teh@stats.ox.ac.uk

Office: 1.16 

Research Groups

Computational Statistics and Machine Learning

Statistical Theory and Methodology