Information-Geometric Decomposition of Generalization Error in Unsupervised Learning
arXiv:2604.12340v1 Announce Type: new
Abstract: We decompose the Kullback–Leibler generalization error (GE) — the expected KL divergence from the data distribution to the trained model — of unsupervised learning into three non-negative components: …