Speaker
Description
Correctly calibrated uncertainties have always been a fundamental pillar of particle physics. As machine learning becomes increasingly integrated into both experimental and theoretical workflows, it is essential that neural network predictions include robust and reliable uncertainty estimates.
This talk will review current approaches to uncertainty estimation in neural networks, focusing on Bayesian neural networks, heteroscedastic loss functions, and repulsive ensembles. Their calibration and practical challenges will be discussed through examples from amplitude regression and unfolding. Additionally, we will explore how machine learning concepts of aleatoric and epistemic uncertainty relate to the statistical and systematic uncertainties familiar in particle physics.