You are all cordially invited to the AMLab seminar on **Thursday Feb 28th** at **16:00** in **C3.163**, where **Christos Louizos** will give a talk titled **“Learning Exchangeable Distributions”**. Afterwards there are the usual drinks and snacks!

**Abstract:** We present a new family of models that directly parametrize exchangeable distributions; it is realized via the introduction of an explicit model for the dependency structure of the joint probability distribution over the data, while respecting the permutation invariance of an exchangeable distribution. This is achieved by combining two recent advances in variational inference and probabilistic modelling for graphs, normalizing flows and (di)graphons. We, empirically, demonstrate that such models are also approximately consistent, hence they can also provide epistemic uncertainty about their predictions without positing an explicit prior over global variables. We show how to train such models on data and evaluate their predictive capabilities as well as the quality of their uncertainty on various tasks.