  We present an improved analysis of mini-batched stochastic dual coordinate
ascent for regularized empirical loss minimization (i.e. SVM and SVM-type
objectives). Our analysis allows for flexible sampling schemes, including where
data is distribute across machines, and combines a dependence on the smoothness
of the loss and/or the data spread (measured through the spectral norm).
