Présidents de session
Opening session, Uncertainty Quantification: Jussieu, Amphi 25
- Mark Neubauer (University of Illinois at Urbana-Champaign)
- Vincent Alexander Croft (LIACS)
I will present a pedagogical introduction to uncertainty modeling in particle physics. I will mostly focus on the methods used at the Large Hadron Collider experiments, where systematic effects are explicitly parameterized in the likelihood function in terms of nuisance parameters. Accurate modeling of systematic effects is of increasing importance at the LHC as the abundant data has decreased...
This talk will focus on a panel of current practices and challenges regarding both Uncertainty Quantification (UQ) and Artificial Intelligence (mainly from the Machine Learning (ML) point of view), in EDF's industrial applications, especially in the topics of risk management of industrial production assets. From our point of view, these two core topics, UQ & AI are, today, closely related to...
In this talk, we delve into the foundational principles of Bayesian optimization, a method particularly well-suited for optimizing deterministic or stochastic functions, whether scalar or vectorial, especially when the evaluation of the function is computationally expensive and no gradient information is available.
Bayesian optimization is particularly relevant in the domains of Design and...
In this talk, we delve into the complexities of uncertainty quantification for neural networks. Model predictions inherently come with uncertainties that arise from several factors: stochastic outcomes, the randomness of training data samples, and the inherent variability of the training process itself. Through the lens of a regression problem, we will unpack these factors and provide a...
The Fair Universe project is building a large-compute-scale AI ecosystem for sharing datasets, training large models and hosting challenges and benchmarks. Furthermore, the project is exploiting this ecosystem for an AI challenge series focused on minimizing the effects of systematic uncertainties in High-Energy Physics (HEP), and on predicting accurate confidence intervals. This talk will...
Machine learning methods have managed to provide significant improvements to data analysis in a multitude of scientific fields. However, as ML finds more and more applications in science, the challenge of quantifying machine learning uncertainties moves into the forefront. This is especially notable in High Energy Physics, where high-precision measurements require precise knowledge of...
