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Publications

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.
Baudry, D. et al. (2025) “Does stochastic gradient really succeed for bandits?”, in.
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].
Hung, E., Mantziou, A. and Reinert, G. (2025) “A Bayesian mixture model for Poisson network autoregression”, Social Network Analysis and Mining, 15(1).
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.
Farghly, T. et al. (2025) “Implicit Regularisation in Diffusion Models: An Algorithm-Dependent Generalisation Analysis”, arXiv.