714 citations · 1.1k across the 48 of their papers we have counts for
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stat.ML2022★ 1 cited
High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data
Konstantin Göbler, Anne Miloschewski, Mathias Drton +1
Graphical models are an important tool in exploring relationships between variables in complex, multivariate data. Methods for learning such graphical models are well developed in…
stat.ML2022★ 1 cited
Learning Linear Non-Gaussian Polytree Models
Daniele Tramontano, Anthea Monod, Mathias Drton
In the context of graphical causal discovery, we adapt the versatile framework of linear non-Gaussian acyclic models (LiNGAMs) to propose new algorithms to efficiently learn graphs…