# Making Multi-Axis Models Robust to Multiplicative Noise: How, and Why?

arXiv: https://arxiv.org/abs/2603.26327 · HTML: https://mlsanity.com/paper/2603.26327/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 42 references that are in the map.
- First posted on arXiv 27 Mar 2026.

## Builds on (most cited first, 4 of 4)

- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [The Sylvester Graphical Lasso (SyGlasso)](https://mlsanity.com/paper/2002.00288/) (2020)
- [GmGM: a Fast Multi-Axis Gaussian Graphical Model](https://mlsanity.com/paper/2211.02920/) (2022)
- [ROBustness In Network (robin): an R package for Comparison and Validation of communities](https://mlsanity.com/paper/2102.03106/) (2021)

## Abstract (arXiv)

In this paper we develop a graph-learning algorithm, MED-MAGMA, to fit multi-axis (Kronecker-sum-structured) models corrupted by multiplicative noise. This type of noise is natural in many application domains, such as that of single-cell RNA sequencing, in which it naturally captures technical biases of RNA sequencing platforms. Our work is evaluated against prior work on each and every public dataset in the Single Cell Expression Atlas under a certain size, demonstrating that our methodology learns networks with better local and global structure. MED-MAGMA is made available as a Python package (MED-MAGMA).