EF Learning-Informed Optimization

Prior-Data Fitted Networks (PFNs) enable zero-shot inference via synthetic pretraining. We extend PFNs by designing tailored priors that encode hierarchical model classes, allowing the network to implicitly perform adaptive Bayesian model selection. Furthermore, we establish rigorous theoretical foundations and explicit generalization guarantees.