OPTIMAL: (O)ptimisation and (P)os(T)-Bayesian (I)nference in (MA)chine (L)earning

The aim of probabilistic machine learning is to find accurate representations of our uncertain beliefs about the world and use them to make better informed decisions. This workshop brings together post-Bayesian approaches to inference and optimisation-based perspectives on uncertainty and decision-making. Post-Bayesian methods address the limitations of classical Bayesian inference by developing alternative inferential principles that remain robust in modern machine-learning settings, where standard modelling assumptions may be violated. Complementing this view, optimisation-based approaches treat inference and decision-making as problems of optimising functionals of probability distributions, providing a unifying framework for both learning probabilistic representations and acting upon them. This workshop welcomes all theoretical and methodological work on how best to represent, find and use probabilistic beliefs about the world.