Recoverability-adaptive Sinkhorn transport restores sample-level survey metadata under a known prior P(M), cutting total variation to <0.001 with modest accuracy loss on CHNS, NHANES, and BRFSS.
MMWR Recomm Rep 52(RR-9):1–12 Peyré G, Cuturi M (2019) Computational optimal transport
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Context Distribution Restoration for Social Surveys: A Recoverability-Adaptive Transport Framework
Recoverability-adaptive Sinkhorn transport restores sample-level survey metadata under a known prior P(M), cutting total variation to <0.001 with modest accuracy loss on CHNS, NHANES, and BRFSS.