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Publications

Lee, H. et al. (2022) “Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility”, arXiv.
Naderiparizi, S. et al. (2022) “Amortized rejection sampling in universal probabilistic programming”, in Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022). Journal of Machine Learning Research, pp. 8392–8412.
Madge, O. et al. (2022) “The COVID-19 pandemic is associated with reduced survival after pancreatic ductal adenocarcinoma diagnosis: A single-centre retrospective analysis”, Journal of Clinical Medicine, 11(9).
Dupont, E., Teh, Y. and Doucet, A. (2022) “Generative models as distributions of functions”, in. Journal of Machine Learning Research, pp. 2989–3015.
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.
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.
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.