Coordinated Multi-Neighborhood Learning on a Directed Acyclic Graph

arXiv:2405.15358v1 Announce Type: cross
Abstract: Learning the structure of causal directed acyclic graphs (DAGs) is useful in many areas of machine learning and artificial intelligence, with wide applications. However, in the high-dimensional setting, it is challenging to obtain good empirical and theoretical results without strong and often restrictive assumptions. Additionally, it is questionable whether all of the variables purported to be included in the network are observable. It is of interest then to restrict consideration to a subset of the …

arxiv cs.lg graph stat.ml type