FAIR Universe HiggsML Uncertainty Challenge Competition
<|reference_start|>FAIR Universe HiggsML Uncertainty Challenge Competition: 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.<|reference_end|>
@article{bhimji2024fair,
    title={FAIR Universe HiggsML Uncertainty Challenge Competition},
    author={Wahid Bhimji and Paolo Calafiura and Ragansu Chakkappai and Yuan-Tang
  Chou and Sascha Diefenbacher and Jordan Dudley and Steven Farrell and Aishik
  Ghosh and Isabelle Guyon and Chris Harris and Shih-Chieh Hsu and Elham E
  Khoda and R'emy Lyscar and Alexandre Michon and Benjamin Nachman and Peter
  Nugent and Mathis Reymond and David Rousseau and Benjamin Sluijter and
  Benjamin Thorne and Ihsan Ullah and Yulei Zhang},
    journal={arXiv preprint arXiv:2410.02867},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02867},
    primaryClass={hep-ph cs.LG hep-ex physics.data-an}
}