# Anderson Acceleration For Bioinformatics-Based Machine Learning

arXiv: https://arxiv.org/abs/2302.00347 · HTML: https://mlsanity.com/paper/2302.00347/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 1 Feb 2023.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima](https://mlsanity.com/paper/1609.04836/) (2016)
- [Practical Quasi-Newton Methods for Training Deep Neural Networks](https://mlsanity.com/paper/2006.08877/) (2020)
- [Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike Sequences](https://mlsanity.com/paper/2109.05019/) (2021)
- [Beyond L1: Faster and Better Sparse Models with skglm](https://mlsanity.com/paper/2204.07826/) (2022)
- [Anderson Acceleration for Reinforcement Learning](https://mlsanity.com/paper/1809.09501/) (2018)
- [GaKCo: a Fast GApped k-mer string Kernel using COunting](https://mlsanity.com/paper/1704.07468/) (2017)
- [Stochastic optimization with momentum: convergence, fluctuations, and traps avoidance](https://mlsanity.com/paper/2012.04002/) (2020)
- [Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning](https://mlsanity.com/paper/1909.03245/) (2019)
- [Anderson Acceleration of Proximal Gradient Methods](https://mlsanity.com/paper/1910.08590/) (2019)
- [Anderson Accelerated Douglas-Rachford Splitting](https://mlsanity.com/paper/1908.11482/) (2019)
- [Anderson acceleration of coordinate descent](https://mlsanity.com/paper/2011.10065/) (2020)
- [Asynchronous Parallel Stochastic Quasi-Newton Methods](https://mlsanity.com/paper/2011.00667/) (2020)
- [Stable Anderson Acceleration for Deep Learning](https://mlsanity.com/paper/2110.14813/) (2021)
- [Safeguarded Anderson acceleration for parametric nonexpansive operators](https://mlsanity.com/paper/2208.02847/) (2022)

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

- [Virus2Vec: Viral Sequence Classification Using Machine Learning](https://mlsanity.com/paper/2304.12328/) (2023)
- [EPIC: Enhancing Privacy through Iterative Collaboration](https://mlsanity.com/paper/2411.05167/) (2024)

## Abstract (arXiv)

Anderson acceleration (AA) is a well-known method for accelerating the convergence of iterative algorithms, with applications in various fields including deep learning and optimization. Despite its popularity in these areas, the effectiveness of AA in classical machine learning classifiers has not been thoroughly studied. Tabular data, in particular, presents a unique challenge for deep learning models, and classical machine learning models are known to perform better in these scenarios. However, the convergence analysis of these models has received limited attention. To address this gap in research, we implement a support vector machine (SVM) classifier variant that incorporates AA to speed up convergence. We evaluate the performance of our SVM with and without Anderson acceleration on several datasets from the biology domain and demonstrate that the use of AA significantly improves convergence and reduces the training loss as the number of iterations increases. Our findings provide a promising perspective on the potential of Anderson acceleration in the training of simple machine learning classifiers and underscore the importance of further research in this area. By showing the effectiveness of AA in this setting, we aim to inspire more studies that explore the applications of AA in classical machine learning.