Siddharth Gopal

Adaptive Sampling for SGD by Exploiting Side Information

This paper proposes a new mechanism for sampling training instances for stochastic gradient descent (SGD) methods by exploiting any side-information associated with the instances (for e.g. class-labels) to improve convergence. Previous methods have either relied on sampling from a distribution defin…