Trans-Glasso: A Transfer Learning Approach to Precision Matrix Estimation

Abstract:

Many real-world systems—ranging from gene regulatory interactions in biology to financial asset dependencies—can be represented by networks, whose edges correspond to conditional relationships among variables. These relationships are succinctly captured by the precision matrix of a multivariate distribution. Estimating the precision matrix is thus fundamental to uncovering the underlying network structure. However, this task can be challenging when the available data for the target domain are limited, undermining accurate inference.