Publications (Computational Statistics and Machine Learning) | Department of Statistics

Publications

Jewson, J., Smith, J. and Holmes, C. (2026) “On the Stability of General Bayesian Inference”, Bayesian Analysis, 21(2), pp. 725–755.

Raja, A. et al. (2026) “QuADMET-Former: Pre-training on Quantum Mechanical Properties Improves ADMET Prediction”, American Chemical Society (ACS).

Mantziou, A. et al. (2026) “GDP nowcasting with large-scale inter-industry payment data in real time: a network approach”, Journal of the Royal Statistical Society Series A (Statistics in Society), pp. qnag061 - qnag061-.

Money-Kyrle, S. et al. (2026) “On Improving Graph Neural Networks for QSAR by Pre-training on Extended-Connectivity Fingerprints”, arXiv.

Aminian, G. et al. (2026) “FraudTransformer: Time-Aware GPT for Digital Payment Fraud Detection”, in. Institute of Electrical and Electronics Engineers (IEEE), pp. 850–856.

Lam, K. et al. (2026) “Metropolis-Adjusted Diffusion Models”, arXiv.

Clerico, E. et al. (2026) “Time-sensitive anytime-valid testing”, arXiv.

Guizar-Coutiño, A. et al. (2026) “Unobserved confounders cannot explain over-crediting in avoided deforestation carbon projects”, Nature Ecology & Evolution, pp. 1–11.

Ashouritaklimi, K., Cortinovis, S. and Caron, F. (2026) “Robust Bayes-assisted conformal prediction”, in. Proceedings of Machine Learning Research.

Prat, A. et al. (2026) “SigmaDock: Untwisting Molecular Docking with Fragment-Based SE(3) Diffusion”, in.