Publications

Dhillon, G., Deligiannidis, G. and Rainforth, T. (2023) “On the expected size of conformal prediction sets”, in. Journal of Machine Learning Research, pp. 1549–1557.

Sinha, S. et al. (2023) “Comparison of machine learning techniques in prediction of mortality following cardiac surgery: analysis of over 220 000 patients from a large national database”, European Journal of Cardio-Thoracic Surgery, 63(6), p. ezad183.

Mantziou, A. et al. (2023) “The GNAR-edge model: A network autoregressive model for networks with time-varying edge weights”, arXiv.

Holmes, C. and Walker, S. (2023) “Statistical inference with exchangeability and martingales”, Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, 381(2247), p. 20220143.

Dunger‐Baldauf, C. et al. (2023) “Generating the Right Evidence at the Right Time: Principles of a New Class of Flexible Augmented Clinical Trial Designs”, Clinical Pharmacology & Therapeutics, 113(5), pp. 1132–1138.

Gaunt, R. and Reinert, G. (2023) “Bounds for the chi-square approximation of Friedman’s statistic by Stein’s method”, Bernoulli - Journal of the Bernoulli Society, 29(3), pp. 2008–2034.

Lee, J., Miscouridou, X. and Caron, F. (2023) “A unified construction for series representations and finite approximations of completely random measures”, Bernoulli, 29(3), pp. 2142–2166.

De Bortoli, V. et al. (2023) “Riemannian score-based generative modelling”, in Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Curran Associates, pp. 2406–2422.

Falck, F. et al. (2023) “A multi-resolution framework for U-nets with applications to hierarchical VAEs”, in Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Curran Associates, pp. 15529–15544.

Xu, W. and Reinert, G. (2023) “A kernelised Stein statistic for assessing implicit generative models”, Advances in Neural Information Processing Systems 35.