Prof. Yee Whye Teh
Prof. Yee Whye Teh’s research interests are in machine learning and computational statistics, in particular scalable learning, probabilistic models, Bayesian nonparametrics and deep learning.
He is particularly interested in theoretically well-founded, but practically relevant, statistical algorithms for learning from data. He has worked on applications in genetics/genomics, text processing, recommender systems, and machine vision.
Selected Publications
Mitrovic, J., Sejdinovic, D. and Teh, Y.W. (2016) ‘DR-ABC: Approximate Bayesian computation with kernel-based distribution regression’, International Conference on Machine Learning (ICML).
Teh, Y.W., Thiery, A.H. and Vollmer, S.J. (2016) ‘Consistency and fluctuations for stochastic gradient Langevin dynamics’, Journal of Machine Learning Research, 17, pp. 1–33.
Favaro, S., Nipoti, B. and Teh, Y.W. (2015) ‘Rediscovery of Good-Turing estimators via Bayesian nonparametrics’, Biometrics, 72(1), pp. 136–145. doi: 10.1111/biom.12366.
Lienert, T.., Whye Teh, Y.., and Doucet, (2015) ‘Expectation Particle Belief Propagation’, Neural Information Processing Systems.
Caron, F., Teh, Y.W. and Murphy, T.B. (2014) ‘Bayesian nonparametric Plackett–Luce models for the analysis of preferences for college degree programmes’, The Annals of Applied Statistics, 8(2), pp. 1145–1181.
Paige, B., Wood, F., Doucet, A. and Teh, Y.W. (2014) ‘Asynchronous anytime Sequential Monte Carlo’, Neural Information Processing Systems (2014).
Lakshminarayanan, B., Roy, D.M. and Teh, Y.W. (2014) ‘Mondrian forests: Efficient online random forests’, Neural Information Processing Systems (2014).
Rao, V. and Teh, Y.W. (2013) ‘Fast MCMC sampling for Markov jump processes and extensions’,Journal of Machine Learning Research, 14, pp. 3295–3320.
