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Publications

Dupont, E., Teh, Y. and Doucet, A. (2022) “Generative models as distributions of functions”, in. Journal of Machine Learning Research, pp. 2989–3015.
Falck, F. et al. (2022) “Multi-facet clustering variational autoencoders”, in Advances in Neural Information Processing Systems 34 (NeurIPS 2021). Curran Associates, pp. 8676–8690.
Falck, F. et al. (2022) “Multi-facet clustering variational autoencoders”, in Advances in Neural Information Processing Systems 34 (NeurIPS 2021). Curran Associates, pp. 8676–8690.
Nicholson, G. et al. (2022) “Interoperability of statistical models in pandemic preparedness: principles and reality.”, Statistical Science, 37(2), pp. 183–206.
Nicholson, G. et al. (2022) “Interoperability of statistical models in pandemic preparedness: principles and reality.”, Statistical Science, 37(2), pp. 183–206.
Nicholson, G. et al. (2022) “Interoperability of statistical models in pandemic preparedness: principles and reality.”, Statistical Science, 37(2), pp. 183–206.
Willetts, M. et al. (2022) “Author Correction: Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants.”, Scientific reports, 12(1), p. 6024.
Parag, K. and Donnelly, C. (2022) “Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers”, PLoS Computational Biology, 18(4).
Unsworth, H. et al. (2022) “Building an evidence standards framework for artificial intelligence-enabled digital health technologies”, The Lancet Digital Health, 4(4), pp. e216 - e217.
Oravkin, E. and Rebeschini, P. (2022) “On optimal interpolation in linear regression”, in. Neural Information Processing Systems Foundation, pp. 29116–29128.