Orateur
Dr
Bruno Loureiro
(ENS Ulm)
Description
Measuring the uncertainty associated to a model's prediction is a central part of statistical practice. In the context of modern deep learning practice, several methods for quantifying the uncertainty of neural networks co-exist. Yet, theoretical guarantees for these methods are scarce in the theoretical literature. In this talk, I will discuss how some of them compare in a mathematically tractable settings where we sharply characterise the statistical properties of the estimators, employing ideas from high-dimensional statistics and statistical physics.
