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Publications

Hung, E., Mantziou, A. and Reinert, G. (2025) “A Bayesian mixture model for Poisson network autoregression”, Social Network Analysis and Mining, 15(1).
Bickford Smith, F. et al. (2025) “Rethinking aleatoric and epistemic uncertainty”, in Proceedings of the 42nd International Conference on Machine Learning. PMLR.
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.
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.
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 ℓp-balls.”
Fatima, A. and Reinert, G. (2025) “Stein’s method for distributions modelling competing and complementary risk problems”, Advances in Applied Probability [Preprint].
Nguyen-Hien, T. et al. (2025) “Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs.”
Liu, X. et al. (2025) “Non-stationary bandit convex optimization: a comprehensive study.”
Adams, J. et al. (2025) “Individualised Counterfactual Examples Using Conformal Prediction Intervals.”