Publications (Computational Statistics and Machine Learning) | Department of Statistics - University of Oxford

Publications

Alfano, C. and Rebeschini, P. (2022) “Linear Convergence for Natural Policy Gradient with Log-linear Policy Parametrization”, arXiv.

Lu, Y., Reinert, G. and Cucuringu, M. (2022) “Trade co-occurrence, trade flow decomposition, and conditional order imbalance in equity markets.”

Fischer, A. et al. (2022) “Normal approximation for the posterior in exponential families.”

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.

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.

Pardo-Diaz, J. et al. (2022) “Generating weighted and thresholded gene coexpression networks using signed distance correlation.”, Network Science, 10(2), pp. 131–145.

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.

Pardo-Diaz, J. et al. (2022) “Extracting information from gene coexpression networks of Rhizobium leguminosarum”, Journal of Computational Biology, 29(7), pp. 752–768.