Publications

Jewson, J., Ghalebikesabi, S. and Holmes, C. (2023) “Differentially Private Statistical Inference through β-Divergence One Posterior Sampling”, in Advances in Neural Information Processing Systems.

Bajaj, S. et al. (2023) “Understanding COVID-19 testing behaviour in England through a sociodemographic lens”, medRxiv.

He, Y. et al. (2022) “MSGNN: a spectral graph neural network based on a novel magnetic signed Laplacian”, in Proceedings of the First Learning on Graphs Conference (LoG 2022). Journal of Machine Learning Research, pp. 40:1 – 40:39.

Fatima, A. and Reinert, G. (2022) “Stein’s method for distributions modelling competing and complementary risk problems.”

Ayed, F., Lee, J. and Caron, F. (2022) “The Normal-Generalised Gamma-Pareto process: a novel pure-jump Levy process with flexible tail and jump-activity properties”, Bayesian Analysis, 19(1), pp. 123–152.

Clarkson, J. et al. (2022) “DAMNETS: a deep autoregressive model for generating Markovian network time series”, in Proceedings of the First Learning on Graphs Conference. Journal of Machine Learning Research, pp. 23:1 – 23:19.

Dupont, E. et al. (2022) “COIN++: neural compression across modalities”, Transactions on Machine Learning Research, 2022(11).

Cooper, J. et al. (2022) “Topological analysis of credit data: preliminary findings”, in Proceedings of the 23rd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL). Springer, pp. 432–442.

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.

Bennett, S., Cucuringu, M. and Reinert, G. (2022) “Lead–lag detection and network clustering for multivariate time series with an application to the US equity market”, Machine Learning, 111(12), pp. 4497–4538.