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.

Camuto, A. et al. (2021) “Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections”, in Proceedings of Machine Learning Research, pp. 1249–1260.

Zaidi, S. et al. (2021) “Neural Ensemble Search for Uncertainty Estimation and Dataset Shift”, in Advances in Neural Information Processing Systems, pp. 7898–7911.

Zhu, X. et al. (2021) “Identification of Underlying Disease Domains by Longitudinal Latent Factor Analysis for Secukinumab Treated Patients in Psoriatic Arthritis and Rheumatoid Arthritis Trials”, in ARTHRITIS & RHEUMATOLOGY, pp. 2516–2518.

Lees, R. et al. (2021) “REVIEWING THE EVIDENCE FOR AND AGAINST SELECTION OF SPECIFIC PYRETHROIDS FOR PROGRAMMATIC PURPOSES”, in AMERICAN JOURNAL OF TROPICAL MEDICINE AND HYGIENE, pp. 432–432.

Nash, R. et al. (2021) “Systematic review of the entomological impact of insecticide-treated nets evaluated using experimental hut trials in Africa”, Current Research in Parasitology and Vector-Borne Diseases, 1.

Cooper, B. et al. (2021) “The burden and dynamics of hospital-acquired SARS-CoV-2 in England”, Research Square.

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

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), p. 28.

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

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