Groups
Category
Normalizing-flow variational inference enriches the variational family by transforming a simple base distribution through a sequence of invertible, differentiable mappings.
PAC-Bayes provides high-probability generalization bounds for randomized predictors by comparing a data-dependent posterior Q to a fixed, data-independent prior P through KL(Q||P).