ELLIS Institute Finland PI positions (second call, winter 2025–26)

Buckling of commercial models alongside maturing of community-led efforts promises major shifts, says Caroline Edwards

Computing quantiles is surprisingly complicated. It gets much weirder when you use weights, and popular software behaves in surprising ways that might trouble you.

Science relies on integrity and trustworthiness. But scientists under career pressure are lured to purchase fake publications from ‘paper mills’ that use AI-generated data, text and image fabrication....

The issues of the traditional ODE solvers in probabilistic models

The Research Council of Finland (RCF) has decided on funding for 15 new Academy Professorships for the period 1 January 2026–31 December 2031. The revised Academy Professorship funding scheme provides funding to researchers who are at the international forefront of their fields and whose research strives for scientific renewal and breakthroughs. The funding is designed to support s...

It is useful to estimate the expected predictive performance of models planned to be used for prediction. We focus on leave-one-out cross-validation (LOO-CV), which has become a popular method for estimating predictive performance of Bayesian models. Given two models, we are interested in comparing the predictive performances and associated uncertainty, which can also be used to compute the probability of one model having better predictive performance than the other model. We study the properties of the Bayesian LOO-CV estimator and the related uncertainty quantification for the predictive performance difference, and analyse when a normal approximation of this uncertainty is well calibrated and whether taking into account higher moments could improve the approximation. We provide new results of the properties both theoretically in the linear regression case and empirically for hierarchical linear, latent linear, and spline models and discuss the challenges. We show that problematic cases include: comparing models with similar predictions, misspecified models, and small data. In these cases, there is a weak connection between the distributions of the LOO-CV estimator and its error. We show that that the problematic skewness of the error distribution for the difference, which occurs when the models make similar predictions, does not fade away when the data size grows to infinity in certain situations. Based on the results, we also provide some practical recommendations for the users of Bayesian LOO-CV for comparing predictive performance of models.

The ELLIS mission is to create a diverse European network that promotes research excellence and advances breakthroughs in AI, as well as a pan-European PhD program to educate the next generation of AI...