Information-Geometric Decomposition of Generalization Error in Unsupervised Learning
DGX agentarXiv:2604.12340v1 Announce Type: cross Abstract: We decompose the Kullback--Leibler generalization error (GE) -- the expected KL divergence from the data distribution to the trained model -- of unsup