  We present a novel framework that can combine multi-domain learning (MDL),
data imputation (DI) and multi-task learning (MTL) to improve performance for
classification and regression tasks in different domains. The core of our
method is an adversarial autoencoder that can: (1) learn to produce
domain-invariant embeddings to reduce the difference between domains; (2) learn
the data distribution for each domain and correctly perform data imputation on
missing data. For MDL, we use the Maximum Mean Discrepancy (MMD) measure to
align the domain distributions. For DI, we use an adversarial approach where a
generator fill in information for missing data and a discriminator tries to
distinguish between real and imputed values. Finally, using the universal
feature representation in the embeddings, we train a classifier using MTL that
given input from any domain, can predict labels for all domains. We demonstrate
the superior performance of our approach compared to other state-of-art methods
in three distinct settings, DG-DI in image recognition with unstructured data,
MTL-DI in grade estimation with structured data and MDMTL-DI in a selection
process using mixed data.
