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hep-ex2026
RINO: Renormalization Group Distillation with No Labels
Zichun Hao, Raghav Kansal, Abhijith Gandrakota +6
Supervised machine learning in high-energy physics (HEP) requires training on labeled simulations of particle collisions, which are known to be unfaithful to the underlying physics…
stat.ML2026
Energy Score-Guided Neural Gaussian Mixture Model for Predictive Uncertainty Quantification
Yang Yang, Chunlin Ji, Haoyang Li +1
Quantifying predictive uncertainty is essential for real world machine learning applications, especially in scenarios requiring reliable and interpretable predictions. Many common…