Publications

Lehmann, B. et al. (2023) “Optimal strategies for learning multi-ancestry polygenic scores vary across traits”, Nature Communications, 14(1), p. 4023.

Ter-Minassian, L. et al. (2023) “PWSHAP: a path-wise explanation model for targeted variables”, in Proceedings of the 40th International Conference on Machine Learning. Proceedings of Machine Learning Research, pp. 34054–34089.

Limnios, S. et al. (2023) “SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation”, arXiv.

Caron, F., Panero, F. and Rousseau, J. (2023) “On sparsity, power-law and clustering properties of graphex processes”, Advances in Applied Probability, 55(4), pp. 1211–1253.

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.

Campbell, A. et al. (2023) “Trans-Dimensional Generative Modeling via Jump Diffusion Models”, 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.