Gunwoong Park

According to our database1, Gunwoong Park authored at least 14 papers between 2015 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2023
Densely connected sub-Gaussian linear structural equation model learning via <i>ℓ</i><sub>1</sub>- and <i>ℓ</i><sub>2</sub>-regularized regressions.
Comput. Stat. Data Anal., May, 2023

Bayesian Approach to Linear Bayesian Networks.
CoRR, 2023

2021
Learning a High-dimensional Linear Structural Equation Model via l1-Regularized Regression.
J. Mach. Learn. Res., 2021

Learning high-dimensional Gaussian linear structural equation models with heterogeneous error variances.
Comput. Stat. Data Anal., 2021

Covariate Correcting Networks for Identifying Associations Between Socioeconomic Factors and Brain Outcomes in Children.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021

2020
Identifiability of Additive Noise Models Using Conditional Variances.
J. Mach. Learn. Res., 2020

2019
High-Dimensional Poisson Structural Equation Model Learning via $\ell_1$-Regularized Regression.
J. Mach. Learn. Res., 2019

Identifiability of Gaussian Structural Equation Models with Homogeneous and Heterogeneous Error Variances.
CoRR, 2019

Identifiability of Generalized Hypergeometric Distribution (GHD) Directed Acyclic Graphical Models.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
High-Dimensional Poisson DAG Model Learning Using ε<sub>1</sub>-Regularized Regression.
CoRR, 2018

Learning Generalized Hypergeometric Distribution (GHD) DAG models.
CoRR, 2018

2017
Learning Quadratic Variance Function (QVF) DAG Models via OverDispersion Scoring (ODS).
J. Mach. Learn. Res., 2017

2016
Identifiability assumptions for directed graphical models with feedback.
CoRR, 2016

2015
Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015


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