Publications

Clerico, E., Flynn, H. and Rebeschini, P. (2026) “Uniform mean estimation for monotonic processes”, Statistics & Probability Letters, 228, pp. 110558–110558.

Aminian, G. et al. (2025) “Generalization and robustness of the tilted empirical risk”, in Proceedings of the 42nd International Conference on Machine Learning. PMLR, pp. 1419–1461.

Kilian, V., Cortinovis, S. and Caron, F. (2025) “Anytime-valid, Bayes-assisted, prediction-powered inference”, in. Neural Information Processing Systems Foundation.

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].

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.

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.

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].