AI RESEARCH
Energy Score-Guided Neural Gaussian Mixture Model for Predictive Uncertainty Quantification
arXiv CS.LG
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ArXi:2603.27672v1 Announce Type: cross Quantifying predictive uncertainty is essential for real world machine learning applications, especially in scenarios requiring reliable and interpretable predictions. Many common parametric approaches rely on neural networks to estimate distribution parameters by optimizing the negative log likelihood. However, these methods often encounter challenges like