077e29b11be80ab57e1a2ecabb7da330-Reviews.html

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper studies a mini-batch gradient method for dual coordinate ascent. The idea is simple: at each iteration randomly pick m samples and update the gradient. The authors prove that the convergence rate of the mini-batch method interpolates between SDCA and AGD -- in certain circumstances it could be faster than both. I am a little surprised that in case of gamma*lambda*n = O(1), the number of examples processed by ASDCA is n*\sqrt{m}, which means that in full parallelization m machines give an acceleration rate of \sqrt{m}.