1 citations · 1 across the 2 of their papers we have counts for
5 papers
Local Fisher Information Enables Sparse Causal Discovery
Byeongguk Kang, Donghyeon Lee, Euijong Song +1
Sparse causal discovery calls for methods that exploit graph structure without estimating high-dimensional densities. We introduce Fisher Information Completion Search (FiCS), a so…
Hold-Out Scoring for Efficient Gaussian DAG Learning
Donguk Shin, Byeongguk Kang, Inseol Lee +1
High-dimensional Gaussian DAG learning faces a statistical-computational gap: methods with sharp sample complexity rely on computationally expensive subset search and a supplied in…
Discrete Score Matching Enables Causal Discovery from Count Data
Euijong Song, Hyewon Park, Gunwoong Park
Count data pose a challenge for score-matching-based causal discovery: derivatives are unavailable, and simply replacing them with finite differences does not generally suffice for…
Convex Distance Operator Transport: A Convex and Geometry-Preserving Formulation
Junhyoung Chung, Euijong Song, Won Hwa Kim +1
We introduce Convex Distance Operator Transport (CDOT), the first convex optimal transport framework that aligns distributions across heterogeneous domains by jointly preserving fe…
Bayesian Approach to Linear Bayesian Networks
Seyong Hwang, Kyoungjae Lee, Sunmin Oh +1
This study proposes the first Bayesian approach for learning high-dimensional linear Bayesian networks. The proposed approach iteratively estimates each element of the topological…