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