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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
stat.ML 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
tBayes-MICE extends MICE with Bayesian MCMC sampling and temporal features to reduce imputation errors and account for uncertainty in time series data.
citing papers explorer
-
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.
-
tBayes-MICE: A Bayesian Approach to Multiple Imputation for Time Series Data
tBayes-MICE extends MICE with Bayesian MCMC sampling and temporal features to reduce imputation errors and account for uncertainty in time series data.