MCFlow: Monte Carlo Flow Models for Data Imputation
It addresses missing data issues in machine learning, offering an incremental improvement for imputation tasks.
The paper tackled data imputation by proposing MCFlow, a deep framework using normalizing flow models and Monte Carlo sampling, which outperformed state-of-the-art methods in imputation quality and data structure preservation.
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.