Salomone, R., South, L.F., Johansen, A.M., Drovandi, C.C., and Kroese, D.P., Unbiased and Consistent Nested Sampling via Sequential Monte Carlo. [arXiv] [code]

Hassan, C., Salomone, R., and Mengersen, K., Deep Generative Models, Synthetic Tabular Data, and Differential Privacy: An Overview and Synthesis. [arXiv]

Hodgkinson, L., van der Heide, C., Salomone, R., Roosta, F., and Mahoney, M., The Interpolating Information Criterion for Overparameterized Models. [arXiv]

Hassan, C., Salomone, R., and Mengersen, K., Federated Variational Inference Methods for Structured Latent Variable Models. [arXiv]

Hodgkinson, L., Salomone, R., and Roosta, F., The reproducing Stein kernel approach for post-hoc corrected sampling. [arXiv]

Schmitz, C., Bradford, J., Salomone, R., and Perrin, D., Leveraging uncertainty quantification to optimise CRISPR guide RNA selection. [preprint]

Fast and scalable off-target assessment for CRISPR guide RNAs using partial matches Schmitz, C., Bradford, J., Salomone, R. , and Perrin, D. [preprint]


Salomone, R., Yu, X., Nott, D., and Kohn, R., Structured Variational Approximations with Skew Normal Decomposable Graphical ModelsJournal of Computational and Graphical Statistics . [paper] [code]

Hodgkinson, L., van der Heide, C., Salomone, R., Roosta, F., and Mahoney, M., A PAC-Bayesian Perspective on the Interpolating Information Criterion (2023). NeurIPS 2023 Workshop on Mathematics of Modern Machine Learning (M3L). [paper]

Wang, X., Jenner, A.L., Salomone, R., Warne, D.J., Drovandi, C. , Calibration of Agent Based Models for Monophasic and Biphasic Tumour Growth using Approximate Bayesian Computation. Journal of Mathematical Biology, 88:28. [paper] [code]

Buchhorn, K., Santos-Fernandez, E., Mengersen, K., and Salomone, R.. Graph Neural Network-Based Anomaly Detection for River Network Systems. (2023), F1000 Research. [paper] [software package]

Bon, J.J., Bretherton, A., Buchhorn, K., Cramb, S., Drovandi, C., Hassan, C., Jenner, A., Mayfield, H.J., McGree, J.M., Mengersen, K., Price, A., Salomone, R., Santos-Fernández, E., Vercelloni, E., and Wang, X. (2023), Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statisticsPhilosophical Transactions of the Royal Society A, 381:2022.0156. [paper]

Davies, L., Salomone, R., Sutton, S., and Drovandi, C. (2023), Transport Reversible Jump Proposals. 26th International Conference on Artificial Intelligence and Statistics (AISTATS).   [paper] [code]

Sutton, M. , Salomone, R., Chevallier, A., and Fearnhead, P. (2022), Continuously-Tempered PDMP Samplers.  Neural Information Processing Systems (NeurIPS), 2022.   [paper] [poster] [code]

Villani, M., Quiroz, M., Kohn, R., and Salomone, R. (2022), Spectral Subsampling MCMC for Stationary Multivariate Time Series with an Application to Vector ARTFIMA Processes. Econometrics and Statistics. [paper]

Hodgkinson, L., Salomone, R. , and Roosta, F. (2021),  Implicit Langevin Algorithms for Sampling From Log-concave Densities, Journal of Machine Learning Research (JMLR) 22: 1-30. [paper]

Salomone R., Quiroz, M., Kohn, R., Villani, M., and Tran, M.N. (2020), Spectral Subsampling MCMC for  Stationary Time Series,  Proceedings of the International Conference on Machine Learning (ICML) 2020.  [paper] [ICML short talk video] [code]

Botev, Z.I., Salomone, R., Mackinlay, D. (2019), Fast and accurate computation of the distribution of sums of dependent log-normals, Annals of Operations Research 280 (1), 19-46. [paper]

Laub, P.J.,Salomone, R., Botev, Z.I. (2019), Monte Carlo estimation of the density of the sum of dependent random variablesMathematics and Computers in Simulation 161, 23-31. [paper] [code]

Salomone, R., Vaisman, R., and Kroese, D.P. (2016). Estimating the Number of Vertices in Convex Polytopes. Proceedings of the Annual International Conference on Operations Research and Statistics, ORS 2016.  [paper]