no code implementations • 27 Nov 2023 • Seyong Hwang, Kyoungjae Lee, Sunmin Oh, Gunwoong Park
The proposed approach iteratively estimates each element of the topological ordering from backward and its parent using the inverse of a partial covariance matrix.
no code implementations • 29 Jan 2019 • Gunwoong Park, Younghwan Kim
It has been shown that linear Gaussian structural equation models are fully identifiable if all error variances are the same or known.
no code implementations • 5 Oct 2018 • Gunwoong Park, Sion Park
In this paper, we develop a new approach to learning high-dimensional Poisson directed acyclic graphical (DAG) models from only observational data without strong assumptions such as faithfulness and strong sparsity.
no code implementations • 8 May 2018 • Gunwoong Park, Hyewon Park
We introduce a new class of identifiable DAG models where the conditional distribution of each node given its parents belongs to a family of generalized hypergeometric distributions (GHD).
no code implementations • 28 Apr 2017 • Gunwoong Park, Garvesh Raskutti
We prove that this class of QVF DAG models is identifiable, and introduce a new algorithm, the OverDispersion Scoring (ODS) algorithm, for learning large-scale QVF DAG models.
no code implementations • 14 Feb 2016 • Gunwoong Park, Garvesh Raskutti
Our simulation study supports our theoretical results, showing that the algorithms based on our two new principles generally out-perform algorithms based on the faithfulness assumption in terms of selecting the true skeleton for DCG models.
no code implementations • NeurIPS 2015 • Gunwoong Park, Garvesh Raskutti
In this paper, we address the question of identifiability and learning algorithms for large-scale Poisson Directed Acyclic Graphical (DAG) models.