Antoine Petitjean
PhD Student
Institute for Theoretical Physics
University of Heidelberg
Introduction
My research:
Flow-based generative models, Machine learning for LHC Theory
My expertise is:
Geometry, probability theory and statistics
A problem I’m grappling with:
Generative unfolding of high-dimensional events
I’ve got my eyes on:
Geometric deep learning incorporating physical insights, foundation models
I want to know more about:
Physics beyond the SM, uncertainty quantification
Projects
Comprehensive uncertainties for generative models
Develop a method to include uncertainties, starting from Bayesian generative networks; expand strategies to model systematic uncertainties using conditional training on nuisance parameters; extend NNPDF methodology for architecture-driven and parameter-driven uncertainties to generative models; study the effect of guided implicit bias on amplification factors between training and generated sample size.
