FAIR Universe HiggsML Uncertainty Challenge Competition
W. Bhimji
P. Calafiura
Ragansu Chakkappai
Yuan-Tang Chou
S. Diefenbacher
Jordan Dudley
S. Farrell
A. Ghosh
Isabelle M Guyon
Chris Harris
Shih-Chieh Hsu
Elham E Khoda
Rémy Lyscar
Alexandre Michon
Benjamin Nachman
P. Nugent
Mathis Reymond
D. Rousseau
Benjamin Sluijter
Benjamin Thorne
Ihsan Ullah
Yulei Zhang

Abstract
The FAIR Universe -- HiggsML Uncertainty Challenge focuses on measuring the physics properties of elementary particles with imperfect simulators due to differences in modelling systematic errors. Additionally, the challenge is leveraging a large-compute-scale AI platform for sharing datasets, training models, and hosting machine learning competitions. Our challenge brings together the physics and machine learning communities to advance our understanding and methodologies in handling systematic (epistemic) uncertainties within AI techniques.
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