Publications

Lee, H. et al. (2023) “Deep neural networks with dependent weights: Gaussian process mixture limit, heavy tails, sparsity and compressibility”, Journal of Machine Learning Research, 24, pp. 1–78.

Lu, X., Rainforth, T. and Teh, Y. (2023) “Daisee: Adaptive importance sampling by balancing exploration and exploitation”, Scandinavian Journal of Statistics, 50(3), pp. 1298–1324.

Whitehead, M. et al. (2023) “Making the invisible visible: what can we do about biased AI in medical devices?”, The BMJ, 382, p. p1893.

Klarner, L. et al. (2023) “Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions”, arXiv.

Armbruster, S. and Reinert, G. (2023) “COVID-19 incidence in the Republic of Ireland: A case study for network-based time series models.”

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.