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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.

Wilde, H. et al. (2021) “Foundations of Bayesian Learning from Synthetic Data”, in Proceedings of Machine Learning Research, pp. 541–549.
Camuto, A. et al. (2021) “Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections”, in Proceedings of Machine Learning Research, pp. 1249–1260.
Fu, H. et al. (2021) “Database of epidemic trends and control measures during the first wave of COVID-19 in mainland China”, International Journal of Infectious Diseases, 102, pp. 463–471.
Ghalebikesabi, S. et al. (2021) “On Locality of Local Explanation Models”, in Advances in Neural Information Processing Systems, pp. 18395–18407.
Fong, E. and Holmes, C. (2021) “Conformal Bayesian Computation”, in Advances in Neural Information Processing Systems, pp. 18268–18279.
Verity, R. et al. (2021) “COVID-19 and the difficulty of inferring epidemiological parameters from clinical data – Authors’ reply”, The Lancet Infectious Diseases, 21(1), pp. 28 – 28.
Zaidi, S. et al. (2021) “Neural Ensemble Search for Uncertainty Estimation and Dataset Shift”, in Advances in Neural Information Processing Systems, pp. 7898–7911.
Mo, Y. et al. (2021) “Transmission dynamics of SARS-CoV-2 in the hospital setting”, medRxiv.
Cooper, B. et al. (2021) “The burden and dynamics of hospital-acquired SARS-CoV-2 in England”, Research Square.
Willetts, M. et al. (2021) “IMPROVING VAES’ ROBUSTNESS TO ADVERSARIAL ATTACK”, in ICLR 2021 - 9th International Conference on Learning Representations.