Groups
Category
Transfer learning theory studies when and why a model trained on a source distribution will work on a different target distribution.
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).