Conveners
Uncertainties & Interpretability
- Martin Klassen (Tufts University (US))
Uncertainties & Interpretability
- Manuel Szewc
We propose a novel framework to obtain asymptotic frequentist uncertainties on machine learned classifier outputs by using model ensembles. With the well-known likelihood trick, this framework can then be applied to the task of density ratio estimation to obtain statistically rigorous frequentist uncertainties on estimated likelihood ratios. As a toy example, we demonstrate that the framework...
Energy correlators have recently shown potential to improve the precision on the top mass precision measurement. However, existing measurement strategies still only use part of the information in the EEEC distribution and rely on arbitrary shape choices. In this talk, we explore the ability of Machine Learning to effectively optimize shape choice and reduce error on the top mass. Specifically,...
ATLAS explores modern neural networks for a multi-dimensional calibration of its calorimeter signal defined by clusters of topologically connected cells (topo-clusters). The Bayesian neural network (BNN) approach yields a continuous and smooth calibration function, including uncertainties on the calibrated energy per topo-cluster. In this talk the performance of this BNN-derived calibration is...
Evidential Deep Learning (EDL) is an uncertainty-aware deep learning approach designed to provide confidence (or epistemic uncertainty) about test data. It treats learning as an evidence acquisition process where more evidence is interpreted as increased predictive confidence. This talk will provide a brief overview of EDL for uncertainty quantification (UQ) and its application to jet tagging...
Generative models are on a fast track to becoming a mainstay in particle physics simulation chains, seeing active work towards adoption by nearly every large experiment and collaboration. However, the question of estimating the uncertainties and statistical expressiveness of samples produced by generative ML models is still far from settled.
Recently, combinations of generative and...
Recently, Kolmogorov-Arnold Networks (KANs) have been proposed as an alternative to multilayer perceptrons, suggesting advantages in performance and interpretability. In this talk, we present the first application of KANs in high-energy physics, focusing on a typical binary classification task involving high-level features.
We study KANs with different depths and widths and include a...
We are presenting the first calibration of the jet pT regression (CMS-DP-2024-064), achieving an expected improvement in jet resolution of up to 17%, and the latest performance results for flavor identification and jet energy resolution estimation using ParticleNet. The pT regression, which focuses on correcting the reconstructed jet pT to the truth-level jet pT, is divided into two...
