  Consider the problem of imputing missing values in a dataset. One the one
hand, conventional approaches using iterative imputation benefit from the
simplicity and customizability of learning conditional distributions directly,
but suffer from the practical requirement for appropriate model specification
of each and every variable. On the other hand, recent methods using deep
generative modeling benefit from the capacity and efficiency of learning with
neural network function approximators, but are often difficult to optimize and
rely on stronger data assumptions. In this work, we study an approach that
marries the advantages of both: We propose *HyperImpute*, a generalized
iterative imputation framework for adaptively and automatically configuring
column-wise models and their hyperparameters. Practically, we provide a
concrete implementation with out-of-the-box learners, optimizers, simulators,
and extensible interfaces. Empirically, we investigate this framework via
comprehensive experiments and sensitivities on a variety of public datasets,
and demonstrate its ability to generate accurate imputations relative to a
strong suite of benchmarks. Contrary to recent work, we believe our findings
constitute a strong defense of the iterative imputation paradigm.
