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Publications

Ghalebikesabi, S. et al. (2022) “Mitigating Statistical Bias within Differentially Private Synthetic Data”, in Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022, pp. 696–705.
Eales, O. et al. (2022) “Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number”, medRxiv.
Miao, N. et al. (2022) “ON INCORPORATING INDUCTIVE BIASES INTO VAES”, in Iclr 2022 10th International Conference on Learning Representations.
Nicholson, G. et al. (2022) “Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework”, Nature Microbiology, 7(1), pp. 97–107.
Nicholson, G. et al. (2022) “Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework”, Nature Microbiology, 7(1), pp. 97–107.
Ghalebikesabi, S. et al. (2022) “Mitigating Statistical Bias within Differentially Private Synthetic Data”, in Proceedings of Machine Learning Research, pp. 685–695.
Parag, K., Donnelly, C. and Zarebski, A. (2022) “Quantifying the information in noisy epidemic curves”, medRxiv.
Penn, M. and Donnelly, C. (2022) “Optimality of Maximal-Effort Vaccination”, medRxiv.
Hillis, S. et al. (2021) “COVID-19-Associated Orphanhood and Caregiver Death in the United States”, arXiv.
Elliott, P. et al. (2021) “Exponential growth, high prevalence of SARS-CoV-2, and vaccine effectiveness associated with the Delta variant”, Science, 374(6574), p. eabl9551.