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

Bailey Andrew, David R. Westhead, Luisa Cutillo
3/27/2026
stat.MEcs.LG

No confident code implementation found for this paper.

Abstract

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).

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Cite this paper

@article{andrew2026making,
  title  = {Making Multi-Axis Models Robust to Multiplicative Noise: How, and Why?},
  author = {Bailey Andrew and David R. Westhead and Luisa Cutillo},
  year   = {2026},
  eprint = {2603.26327},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2603.26327v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2603.26327.svg)](https://codeofpaper.com/paper/2603.26327)

Discussion