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Data imputation has been extensively explored to solve the missing dataproblem. The dramatically increasing volume of incomplete data makes theimputation models computationally infeasible in many real-life applications. Inthis paper, we propose an effective scalable imputation system named SCIS tosignificantly speed up the training of the differentiable generativeadversarial imputation models under accuracy-guarantees for large-scaleincomplete data. SCIS consists of two modules, differentiable imputationmodeling (DIM) and sample size estimation (SSE). DIM leverages a new maskingSinkhorn divergence function to make an arbitrary generative adversarialimputation model differentiable, while for such a differentiable imputationmodel, SSE can estimate an appropriate sample size to ensure the user-specifiedimputation accuracy of the final model. Extensive experiments upon severalreal-life large-scale datasets demonstrate that, our proposed system canaccelerate the generative adversarial model training by 7.1x. Using around 7.6%samples, SCIS yields competitive accuracy with the state-of-the-art imputationmethods in a much shorter computation time.

Differentiable and Scalable Generative Adversarial Models for Data Imputation