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Author

Yijiao Zhang

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Preprint Aug 2026

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.

Yijiao Zhang, Hongzhe Li · 0 citations

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