Generation-Powered Inference for Distribution-Valued Outcomes
This work proposes generation-powered inference (GPI), a general framework for improving inference on distribution-valued parameters using auxiliary generative models, focusing on Wasserstein barycenters and related distributional functionals, and introduces a function-valued bridge representation.