Distributed Mini-Batch SDCA

This work provides an incremental improvement in optimization theory for machine learning practitioners working with distributed or large-scale datasets.

The paper tackles the problem of improving the analysis of mini-batched stochastic dual coordinate ascent for regularized empirical loss minimization, such as SVM objectives, by allowing flexible sampling schemes including distributed data and incorporating dependencies on loss smoothness and data spread.