Publications

Johnson, E., Pike-Burke, C. and Rebeschini, P. (2023) “Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity”, arXiv.

Alfano, C., Yuan, R. and Rebeschini, P. (2023) “A novel framework for policy mirror descent with general parameterization and linear convergence”, in. Curran Associates.

Hunter, D. and Holmes, C. (2023) “Where medical statistics meets artificial intelligence”, New England Journal of Medicine, 389(13), pp. 1211–1219.

Temčinas, T., Nanda, V. and Reinert, G. (2023) “Goodness-of-fit via Count Statistics in Dense Random Simplicial Complexes”, arXiv.

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.”