3 papers
stat.ME2026
Making Multi-Axis Models Robust to Multiplicative Noise: How, and Why?
Bailey Andrew, David R. Westhead, Luisa Cutillo
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…
stat.ME2024
Graphical Modelling without Independence Assumptions for Uncentered Data
Bailey Andrew, David R. Westhead, Luisa Cutillo
The independence assumption is a useful tool to increase the tractability of one's modelling framework. However, this assumption does not match reality; failing to take dependencie…
stat.ML2024
Making Multi-Axis Gaussian Graphical Models Scalable to Millions of Cells
Bailey Andrew, Erica L. Harris, James A. Poulter +2
Motivation: Networks underlie the generation and interpretation of many biological datasets: gene networks shed light on the regulatory structure of the genome, and cell networks c…