  In this paper we develop dual free mini-batch SDCA with adaptive
probabilities for regularized empirical risk minimization. This work is
motivated by recent work of Shai Shalev-Shwartz on dual free SDCA method,
however, we allow a non-uniform selection of "dual" coordinates in SDCA.
Moreover, the probability can change over time, making it more efficient than
fix uniform or non-uniform selection. We also propose an efficient procedure to
generate a random non-uniform mini-batch through iterative process. The work is
concluded with multiple numerical experiments to show the efficiency of
proposed algorithms.
