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Publications

Zhang, R. and Reinert, G. (2024) “Simulating weak attacks in a new duplication–divergence model with node loss”, Entropy, 26(10).
Pigoli, D. et al. (2024) “Assessing the Performance of Machine Learning Methods Trained on Public Health Observational Data: A Case Study From COVID‐19”, Statistics in Medicine [Preprint].
Pigoli, D. et al. (2024) “Assessing the Performance of Machine Learning Methods Trained on Public Health Observational Data: A Case Study From COVID‐19”, Statistics in Medicine [Preprint].
Klarner, L. et al. (2024) “Context-guided diffusion for out-of-distribution molecular and protein design”, in Proceedings of the 41st International Conference on Machine Learning. PMLR, pp. 24770–24807.
Domingo, E. et al. (2024) “Identification and validation of a machine learning model of complete response to radiation in rectal cancer reveals immune infiltrate and TGFβ as key predictors”, EBioMedicine, 106, pp. 105228 – 105228.
Naulet, Z., Rousseau, J. and Caron, F. (2024) “Asymptotic analysis of statistical estimators related to MultiGraphex processes under misspecification”, Bernoulli, 30(4), pp. 2644–2675.
Aminian, G. et al. (2024) “Generalization error of graph neural networks in the mean-field regime”, in Proceedings of the 41st International Conference on Machine Learning (ICML 2024). Proceedings of Machine Learning Research, pp. 1359–1391.
Bajaj, S. et al. (2024) “Understanding COVID-19 testing behaviour in England through a sociodemographic lens: a population based study”, Lancet Digital Health [Preprint].
Klarner, L. et al. (2024) “Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design”, arXiv.
Clarkson, J. et al. (2024) “Split Conformal Prediction under Data Contamination”, arXiv.