Papers › MCFlow: Monte Carlo Flow Models for Data Imputation

MCFlow: Monte Carlo Flow Models for Data Imputation

27 Mar 2020CVPR 2020 6arXiv:2003.12628archive 2025-07-28

Trevor W. Richardson, Wencheng Wu, Lei Lin, Beilei Xu, Edgar A. Bernal

We consider the topic of data imputation, a foundational task in machine learning that addresses issues with missing data. To that end, we propose MCFlow, a deep framework for imputation that leverages normalizing flow generative models and Monte Carlo sampling. We address the causality dilemma that arises when training models with incomplete data by introducing an iterative learning scheme which alternately updates the density estimate and the values of the missing entries in the training data. We provide extensive empirical validation of the effectiveness of the proposed method on standard multivariate and image datasets, and benchmark its performance against state-of-the-art alternatives. We demonstrate that MCFlow is superior to competing methods in terms of the quality of the imputed data, as well as with regards to its ability to preserve the semantic structure of the data.

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