Publications

Baudry, D. et al. (2025) “Does stochastic gradient really succeed for bandits?”, in.

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

Alfano, C. et al. (2025) “Meta-Learning Objectives for Preference Optimization”, in.

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].

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 $\boldsymbol{\ell_p}$-balls.”