Split Conformal Prediction for Regression (Christian Igel)

Abstract: This tutorial introduces Conformal Prediction (CP) for quantifying the uncertainty of machine learning (ML) models. Conformal Prediction provides prediction sets guaranteed to contain (in expectation) the true outcome with a user-specified probability without strong assumptions about the underlying data distribution. We focus on regression tasks and on Split CP, which leverages a hold-out calibration data set and can be applied to any ML model. We establish the foundational theory of Split CP, proving its marginal coverage guarantee under the assumption of i.i.d. data. Then we present Split Localized Conformal Prediction, an efficient method that approximates conditional coverage by adapting to the local data structure while preserving the rigorous marginal guarantee. The goal of this tutorial is to provide attendees with the basic foundations needed to apply CP in their research and to explore more advanced topics in distribution-free uncertainty quantification.

Model selection and uncertainty quantification in Bayesian deep learning (Søren Hauberg)

Abstract: We will discuss the basics of Bayesian deep learning to build a pipeline for model selection and uncertainty quantification. We will gradually transition to discuss why standard approximations fails for overparametrized models and what to do about it

The exercises will be in form of a team challenge.

You will be divided into teams at the start of the summer school.