Robust Model Selection of Gaussian Graphical Models

arXiv:2211.05690v2 Announce Type: replace-cross
Abstract: In Gaussian graphical model selection, noise-corrupted samples present significant challenges. It is known that even minimal amounts of noise can obscure the underlying structure, leading to fundamental identifiability issues. A recent line of work addressing this "robust model selection" problem narrows its focus to tree-structured graphical models. Even within this specific class of models, exact structure recovery is shown to be impossible. However, several algorithms have been developed that are known to provably recover the …

abstract arxiv challenges cs.lg focus fundamental line math.st model selection noise robust samples stat.ml stat.th tree type work