12 citations · 24 across the 6 of their papers we have counts for
7 papers
The Statistical Cost of Causal Discovery with Feedback
Sunmin Oh, Seungsu Han, Gunwoong Park
What determines the unavoidable sample cost of learning cyclic causal structure? For cyclic linear non-Gaussian models, we study exact condensation recovery from observational data…
Relaxed Sparsest-Permutation Formulation for Causal Discovery at Scale
Sunmin Oh, Sang-Yun Oh, Gunwoong Park
Despite the growing availability of large datasets, causal structure learning remains computationally prohibitive at scale. We revisit sparsest-permutation learning for linear stru…
Identifiability of Gaussian Structural Equation Models with Homogeneous and Heterogeneous Error Variances
Gunwoong Park, Younghwan Kim
In this work, we consider the identifiability assumption of Gaussian linear structural equation models (SEMs) in which each variable is determined by a linear function of its paren…
High-Dimensional Poisson DAG Model Learning Using -Regularized Regression
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 a…
Identifiability of Generalized Hypergeometric Distribution (GHD) Directed Acyclic Graphical Models
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 distribution…
Learning Quadratic Variance Function (QVF) DAG models via OverDispersion Scoring (ODS)
Gunwoong Park, Garvesh Raskutti
Learning DAG or Bayesian network models is an important problem in multi-variate causal inference. However, a number of challenges arises in learning large-scale DAG models includi…
Identifiability Assumptions and Algorithm for Directed Graphical Models with Feedback
Gunwoong Park, Garvesh Raskutti
Directed graphical models provide a useful framework for modeling causal or directional relationships for multivariate data. Prior work has largely focused on identifiability and s…