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Publications

Wu, F. and Rebeschini, P. (2022) “Nearly minimax-optimal rates for noisy sparse phase retrieval via early-stopped mirror descent”, Information and Inference: a Journal of the IMA, 12(2), pp. 633–713.
Teh, Y. et al. (2022) “Authors’ Reply to the Discussion of ‘Efficient Bayesian Inference of Instantaneous Reproduction Numbers at Fine Spatial Scales, with an Application to Mapping and Nowcasting the Covid-19 Epidemic in British Local Authorities’ by Teh et al. in Session 2 of the Royal Statistical Society’s Special Topic Meeting on COVID-19 Transmission: 11 June 2021”, Journal of the Royal Statistical Society Series A (Statistics in Society), 185(Supplement_1), pp. s107 - s109.
Alfano, C. and Rebeschini, P. (2022) “Linear Convergence for Natural Policy Gradient with Log-linear Policy Parametrization”, arXiv.
Martin, N. et al. (2022) “A graph based neural network approach to immune profiling of multiplexed tissue samples”, pp. 3063–3067.
Tirumala, D. et al. (2022) “Behavior Priors for Efficient Reinforcement Learning”, Journal of Machine Learning Research, 23.
Nicholson, G. et al. (2022) “Multivariate phenotype analysis enables genome-wide inference of mammalian gene function”, PLOS Biology, 20(8), p. e3001723.
Nicholson, G. et al. (2022) “Multivariate phenotype analysis enables genome-wide inference of mammalian gene function”, PLOS Biology, 20(8), p. e3001723.
Jersakova, R. et al. (2022) “Bayesian Imputation of COVID-19 Positive Test Counts for Nowcasting Under Reporting Lag”, Journal of the Royal Statistical Society Series C (Applied Statistics), 71(4), pp. 834–860.
Jersakova, R. et al. (2022) “Bayesian Imputation of COVID-19 Positive Test Counts for Nowcasting Under Reporting Lag”, Journal of the Royal Statistical Society Series C (Applied Statistics), 71(4), pp. 834–860.
Benedetto, U. et al. (2022) “Machine learning improves mortality risk prediction after cardiac surgery: Systematic review and meta-analysis”, Journal of Thoracic and Cardiovascular Surgery, 163(Int Stat Rev 80 2012), pp. 2075 – 2087.e9.