AI RESEARCH
Amortized Vine Copulas for High-Dimensional Density and Information Estimation
arXiv CS.LG
•
ArXi:2604.20568v1 Announce Type: new Modeling high-dimensional dependencies while keeping likelihoods tractable remains challenging. Classical vine-copula pipelines are interpretable but can be expensive, while many neural estimators are flexible but less structured. In this work, we propose Vine Denoising Copula (VDC), an amortized vine-copula pipeline that trains a single bivariate denoising model and reuses it across all vine edges. For each edge, given pseudo-observations, the model predicts a density grid.