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

ArXi:2603.26327v1 Announce Type: cross 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, nstrating that our methodology learns networks with better local and global structure.