Publications by Statistical Genetics and Epidemiology

Our research spans areas of statistical genetics, in particular the development of powerful statistical approaches to analyse genetic data, as well as studying infectious diseases.

Ghalebikesabi, S. et al. (2021) “Deep Generative Missingness Pattern-Set Mixture Models”, in Proceedings of Machine Learning Research, pp. 3727–3735.

Ghalebikesabi, S. et al. (2021) “On Locality of Local Explanation Models”, in Advances in Neural Information Processing Systems, pp. 18395–18407.

Willetts, M. et al. (2021) “IMPROVING VAES’ ROBUSTNESS TO ADVERSARIAL ATTACK”, in ICLR 2021 - 9th International Conference on Learning Representations.

Wilde, H. et al. (2021) “Foundations of Bayesian Learning from Synthetic Data”, in Proceedings of Machine Learning Research, pp. 541–549.

Falck, F. et al. (2021) “Multi-Facet Clustering Variational Autoencoders”, in Advances in Neural Information Processing Systems, pp. 8676–8690.

Payne, A. et al. (2021) “Burns Management at A UK Major Trauma Centre During The COVID-19 Lockdown”, in BRITISH JOURNAL OF SURGERY.

Willetts, M., Roberts, S. and Holmes, C. (2020) “Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels”, in. Institute of Electrical and Electronics Engineers (IEEE), pp. 5286–5295.

Kendall, M. et al. (2020) “Epidemiological changes on the Isle of Wight after the launch of the NHS Test and Trace programme: a preliminary analysis”, The Lancet Digital Health, 2(12), pp. e658 - e666.

Watson, J. and Holmes, C. (2020) “Machine learning analysis plans for randomised controlled trials: detecting treatment effect heterogeneity with strict control of type I error”, Trials, 21(1), p. 156.