Publications

Lu, X. et al. (2017) “Relativistic Monte Carlo”, in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017.

Maddison, C., Mnih, A. and Teh, Y. (2017) “The concrete distribution: A continuous relaxation of discrete random variables”, in 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings.

Mitrovic, J., Sejdinovic, D. and Teh, Y. (2017) “Deep kernel machines via the kernel reparametrization trick”, in 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings.

Maddison, C. et al. (2017) “Particle value functions”, in 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings.

Teh, Y. et al. (2017) “Distral: robust multitask reinforcement learning”, in Advances in Neural Information Processing Systems 30 (NIPS 2017). Massachusetts Institute of Technology Press, pp. 4497–4507.

Rukat, T. et al. (2017) “Bayesian boolean matrix factorisation”, in 34th International Conference on Machine Learning, ICML 2017, pp. 4539–4549.

Esperança, P., Aslett, L. and Holmes, C. (2017) “Encrypted accelerated least squares regression”, in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017.

Battiston, M., Favaro, S. and Teh, Y. (2016) “Multi-armed bandit for species discovery: a Bayesian nonparametric approach”, Journal of the American Statistical Association, 113(521), pp. 455–466.

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.

Nicholson, G. and Holmes, C. (2016) “A note on statistical repeatability and study design for high-throughput assays”, Statistics in Medicine, 36(5), pp. 790–798.