# Distributed Mini-Batch SDCA

arXiv: https://arxiv.org/abs/1507.08322 · HTML: https://mlsanity.com/paper/1507.08322/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2015.

## Built on by (most cited first, 2 of 2)

- [Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization](https://mlsanity.com/paper/2106.03034/) (2021)
- [Learning Under Delayed Feedback: Implicitly Adapting to Gradient Delays](https://mlsanity.com/paper/2106.12261/) (2021)

## Abstract (arXiv)

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).