Publications

Ganjgahi, H. et al. (2025) “AI-driven reclassification of multiple sclerosis progression”, Nature Medicine, 31(10), pp. 3414–3424.

Temcinas, T., Nanda, V. and Reinert, G. (2025) “Goodness-of-fit via count statistics in dense random simplicial complexes”, Foundations of Data Science [Preprint].

Pituk, G., Shirvaikar, V. and Rainforth, T. (2025) “Do Bayesian neural networks actually behave like Bayesian models?”, in Proceedings of the 42nd International Conference on Machine Learning. PMLR, pp. 49420–49458.

Hedman, M. et al. (2025) “Step-DAD: semi-amortized policy-based Bayesian experimental design”, in Proceedings of the 42nd International Conference on Machine Learning. PMLR, pp. 22904–22923.

Bickford Smith, F. et al. (2025) “Rethinking aleatoric and epistemic uncertainty”, in Proceedings of the 42nd International Conference on Machine Learning. PMLR.

Farghly, T. et al. (2025) “Implicit Regularisation in Diffusion Models: An Algorithm-Dependent Generalisation Analysis”, arXiv.

Johnson, E. et al. (2025) “On the necessity of adaptive regularisation:Optimal anytime online learning on $\boldsymbol{\ell_p}$-balls.”

Fatima, A. and Reinert, G. (2025) “Stein’s method for distributions modelling competing and complementary risk problems”, Advances in Applied Probability [Preprint].

Reinert, G., Hung, E. and Mantziou, A. (2025) “A Bayesian mixture model for Poisson network autoregression”, Social Network Analysis and Mining [Preprint].

Liu, X. et al. (2025) “Non-stationary Bandit Convex Optimization: A Comprehensive Study.”