SCORE: Spectral Correlation Estimation for Multivariate Gaussians
SCORE is a framework for predicting high-dimensional Gaussian data by separating marginal distribution learning from modeling structured correlations. It uses a stable Gaussian kernel score instead of standard negative log-likelihood to ensure optimization stability. The method employs a two-stage process, estimating marginals first and then modeling the correlation structure in spectral space, resulting in superior calibration.