FLOWGEM generates complete data under non-monotone MAR missingness by discretizing Wasserstein gradient flows with a local linear density-ratio estimator to minimize expected KL divergence over missingness patterns.
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Generative Modeling under Non-Monotone MAR Missingness via Approximate Wasserstein Gradient Flows
FLOWGEM generates complete data under non-monotone MAR missingness by discretizing Wasserstein gradient flows with a local linear density-ratio estimator to minimize expected KL divergence over missingness patterns.