Review for NeurIPS paper: Handling Missing Data with Graph Representation Learning

Dear authors, The reviewers discussed your document and carefully considered your rebuttal. All agree that the main contribution is the framework for dealing with missing values using bipartite graphs. This is an interesting idea, both for imputing missing values and for making predictions with missing values. They also appreciated that you added experimental comparisons to two reference methods (missMDA and MIWAE) and included the results in your response, as well as experiments on two additional high-dimensional data sets. Nevertheless, although they emphasized that GNNs are used here as a toolbox and not as the focus of the study, you need to be specific about important aspects of their application (such as discussions of architectural novelty and scalability), as noted by two reviewers.