Generative Modeling under Non-Monotonic MAR Missingness via Approximate Wasserstein Gradient Flows

ArXi:2604.04567v1 Announce Type: cross The prevalence of missing values in data science poses a substantial risk to any further analyses. Despite a wealth of research, principled nonparametric methods to deal with general non-monotone missingness are still scarce. Instead, ad-hoc imputation methods are often used, for which it remains unclear whether the correct distribution can be recovered. In this paper, we propose FLOWGEM, a principled iterative method for generating a complete dataset from a dataset with values Missing at Random.