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stat.ML2026
Generative Modeling under Non-Monotone MAR Missingness via Approximate Wasserstein Gradient Flows
Gitte Kremling, Jeffrey Näf, Johannes Lederer
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 gener…
stat.ML2025
Non-asymptotic error bounds for probability flow ODEs under weak log-concavity
Gitte Kremling, Francesco Iafrate, Mahsa Taheri +1
Score-based generative modeling, implemented through probability flow ODEs, has shown impressive results in numerous practical settings. However, most convergence guarantees rely o…