  A major hurdle in machine learning is scalability to massive datasets.
Approaches to overcome this hurdle include compression of the data matrix and
distributing the computations. \textit{Leverage score sampling} provides a
compressed approximation of a data matrix using an importance weighted subset.
\textit{Gradient coding} has been recently proposed in distributed optimization
to compute the gradient using multiple unreliable worker nodes. By designing
coding matrices, gradient coded computations can be made resilient to
stragglers, which are nodes in a distributed network that degrade system
performance. We present a novel \textit{weighted leverage score} approach, that
achieves improved performance for distributed gradient coding by utilizing an
importance sampling.
