  Adaptive moment methods have been remarkably successful in deep learning
optimization, particularly in the presence of noisy and/or sparse gradients. We
further the advantages of adaptive moment techniques by proposing a family of
double adaptive stochastic gradient methods~\textsc{DASGrad}. They leverage the
complementary ideas of the adaptive moment algorithms widely used by deep
learning community, and recent advances in adaptive probabilistic algorithms.We
analyze the theoretical convergence improvements of our approach in a
stochastic convex optimization setting, and provide empirical validation of our
findings with convex and non convex objectives. We observe that the benefits
of~\textsc{DASGrad} increase with the model complexity and variability of the
gradients, and we explore the resulting utility in extensions of
distribution-matching multitask learning.
