  Modern scientific research and applications very often encounter "fragmentary
data" which brings big challenges to imputation and prediction. By leveraging
the structure of response patterns, we propose a unified and flexible framework
based on Generative Adversarial Nets (GAN) to deal with fragmentary data
imputation and label prediction at the same time. Unlike most of the other
generative model based imputation methods that either have no theoretical
guarantee or only consider Missing Completed At Random (MCAR), the proposed
FragmGAN has theoretical guarantees for imputation with data Missing At Random
(MAR) while no hint mechanism is needed. FragmGAN trains a predictor with the
generator and discriminator simultaneously. This linkage mechanism shows
significant advantages for predictive performances in extensive experiments.
