  We study the problem of imputing missing values in a dataset, which has
important applications in many domains. The key to missing value imputation is
to capture the data distribution with incomplete samples and impute the missing
values accordingly. In this paper, by leveraging the fact that any two batches
of data with missing values come from the same data distribution, we propose to
impute the missing values of two batches of samples by transforming them into a
latent space through deep invertible functions and matching them
distributionally. To learn the transformations and impute the missing values
simultaneously, a simple and well-motivated algorithm is proposed. Our
algorithm has fewer hyperparameters to fine-tune and generates high-quality
imputations regardless of how missing values are generated. Extensive
experiments over a large number of datasets and competing benchmark algorithms
show that our method achieves state-of-the-art performance.
