# Distributed Block-diagonal Approximation Methods for Regularized Empirical Risk Minimization

arXiv: https://arxiv.org/abs/1709.03043 · HTML: https://mlsanity.com/paper/1709.03043/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2017.

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

- [An Exact Solver for the Weston-Watkins SVM Subproblem](https://mlsanity.com/paper/2102.05640/) (2021)
- [On the Relationship Between CoCoA and ADMM for Distributed Empirical Risk Minimization](https://mlsanity.com/paper/2502.00470/) (2025)

## Abstract (arXiv)

In recent years, there is a growing need to train machine learning models on a huge volume of data. Designing efficient distributed optimization algorithms for empirical risk minimization (ERM) has therefore become an active and challenging research topic. In this paper, we propose a flexible framework for distributed ERM training through solving the dual problem, which provides a unified description and comparison of existing methods. Our approach requires only approximate solutions of the sub-problems involved in the optimization process, and is versatile to be applied on many large-scale machine learning problems including classification, regression, and structured prediction. We show that our approach enjoys global linear convergence for a broader class of problems, and achieves faster empirical performance, compared with existing works.