Publications

Pardo-Diaz, J. et al. (2022) “Generating weighted and thresholded gene coexpression networks using signed distance correlation.”, Network Science, 10(2), pp. 131–145.

Benedetto, U. et al. (2022) “Machine learning improves mortality risk prediction after cardiac surgery: Systematic review and meta-analysis”, Journal of Thoracic and Cardiovascular Surgery, 163(Int Stat Rev 80 2012), pp. 2075 – 2087.e9.

Campbell, A. et al. (2022) “Online variational filtering and parameter learning”, in Advances in Neural Information Processing Systems 34 (NeurIPS 2021). Curran Associates, pp. 18633–18645.

Campbell, A. et al. (2022) “A Continuous Time Framework for Discrete Denoising Models”, arXiv.

Pardo-Diaz, J. et al. (2022) “Extracting information from gene coexpression networks of Rhizobium leguminosarum”, Journal of Computational Biology, 29(7), pp. 752–768.

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.