Publications

Nicholson, G. et al. (2021) “Interoperability of statistical models in pandemic preparedness: principles and reality”, arXiv.

Alfano, C. and Rebeschini, P. (2021) “Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learning”, arXiv.

Wu, F. and Rebeschini, P. (2021) “Hadamard wirtinger flow for sparse phase retrieval”, in Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021). Journal of Machine Learning Research, pp. 982–990.

Richards, D., Dobriban, E. and Rebeschini, P. (2021) “Comparing Classes of Estimators: When does Gradient Descent Beat Ridge Regression in Linear Models?”, arXiv.

Jiang, X., Holmes, C. and McVean, G. (2021) “The impact of age on genetic risk for common diseases”, PLOS Genetics, 17(8).

Ayed, F. and Caron, F. (2021) “Nonnegative Bayesian nonparametric factor models with completely random measures”, Statistics and Computing, 31(5).

Watson, J. et al. (2021) “Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision”, eLife, 10.

Fang, S. et al. (2021) “A Bayesian semi-parametric model for thermal proteome profiling”, Communications Biology, 4(1), p. 810.

Wymant, C. et al. (2021) “The epidemiological impact of the NHS COVID-19 app”, Nature, 594(7863), pp. 408–412.

Chau, S. et al. (2021) “BayesIMP: Uncertainty Quantification for Causal Data Fusion”, arXiv.