# Snacks: a fast large-scale kernel SVM solver

arXiv: https://arxiv.org/abs/2304.07983 · HTML: https://mlsanity.com/paper/2304.07983/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 17 Apr 2023.

## Builds on (most cited first, 12 of 12)

- [Generalization Properties of Learning with Random Features](https://mlsanity.com/paper/1602.04474/) (2016)
- [Less is More: Nystr"om Computational Regularization](https://mlsanity.com/paper/1507.04717/) (2015)
- [Kernel methods through the roof: handling billions of points efficiently](https://mlsanity.com/paper/2006.10350/) (2020)
- [FALKON: An Optimal Large Scale Kernel Method](https://mlsanity.com/paper/1705.10958/) (2017)
- [Acceleration Methods](https://mlsanity.com/paper/2101.09545/) (2021)
- [On Fast Leverage Score Sampling and Optimal Learning](https://mlsanity.com/paper/1810.13258/) (2018)
- [But How Does It Work in Theory? Linear SVM with Random Features](https://mlsanity.com/paper/1809.04481/) (2018)
- [The Nystr"om method for convex loss functions](https://mlsanity.com/paper/2006.10016/) (2020)
- [liquidSVM: A Fast and Versatile SVM package](https://mlsanity.com/paper/1702.06899/) (2017)
- [Statistical and Computational Trade-Offs in Kernel K-Means](https://mlsanity.com/paper/1908.10284/) (2019)
- [ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions](https://mlsanity.com/paper/2106.12231/) (2021)
- [Accelerate Stochastic Subgradient Method by Leveraging Local Growth Condition](https://mlsanity.com/paper/1607.01027/) (2016)

## Abstract (arXiv)

Kernel methods provide a powerful framework for non parametric learning. They are based on kernel functions and allow learning in a rich functional space while applying linear statistical learning tools, such as Ridge Regression or Support Vector Machines. However, standard kernel methods suffer from a quadratic time and memory complexity in the number of data points and thus have limited applications in large-scale learning. In this paper, we propose Snacks, a new large-scale solver for Kernel Support Vector Machines. Specifically, Snacks relies on a Nystr"om approximation of the kernel matrix and an accelerated variant of the stochastic subgradient method. We demonstrate formally through a detailed empirical evaluation, that it competes with other SVM solvers on a variety of benchmark datasets.