# Scalable Optimal Margin Distribution Machine

arXiv: https://arxiv.org/abs/2305.04837 · HTML: https://mlsanity.com/paper/2305.04837/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 43 references that are in the map.
- First posted on arXiv 8 May 2023.

## Builds on (most cited first, 3 of 3)

- [Optimal Margin Distribution Machine](https://mlsanity.com/paper/1604.03348/) (2016)
- [Margin-Based Generalization Lower Bounds for Boosted Classifiers](https://mlsanity.com/paper/1909.12518/) (2019)
- [A Divide-and-Conquer Solver for Kernel Support Vector Machines](https://mlsanity.com/paper/1311.0914/) (2013)

## Built on by (most cited first, 1 of 1)

- [Structure-Preserving Margin Distribution Learning for High-Order Tensor Data with Low-Rank Decomposition](https://mlsanity.com/paper/2509.14577/) (2025)

## Abstract (arXiv)

Optimal margin Distribution Machine (ODM) is a newly proposed statistical learning framework rooting in the novel margin theory, which demonstrates better generalization performance than the traditional large margin based counterparts. Nonetheless, it suffers from the ubiquitous scalability problem regarding both computation time and memory as other kernel methods. This paper proposes a scalable ODM, which can achieve nearly ten times speedup compared to the original ODM training method. For nonlinear kernels, we propose a novel distribution-aware partition method to make the local ODM trained on each partition be close and converge fast to the global one. When linear kernel is applied, we extend a communication efficient SVRG method to accelerate the training further. Extensive empirical studies validate that our proposed method is highly computational efficient and almost never worsen the generalization.