4 citations · 4 across the 8 of their papers we have counts for
8 papers
Leveraging heterogeneity for identifiability: Bayesian order-based learning of multiple DAGs
Hyunwoong Chang, Fariha Taskin
We propose a joint order-based scoring framework for causal structure learning of directed acyclic graph (DAG) models under heterogeneous data settings. We show that leveraging het…
A Bayesian Framework for Symmetry Inference in Chaotic Attractors
Ziad Ghanem, Chang Hyunwoong, Preskella Mrad
Detecting symmetry from data is a fundamental problem in signal analysis, providing insight into underlying structure and constraints. When data emerge as trajectories of dynamical…
Identifiability of the minimum-trace directed acyclic graph and hill climbing algorithms without strict local optima under weakly increasing error variances
Hyunwoong Chang, Jaehoan Kim
We prove that the true underlying directed acyclic graph (DAG) in Gaussian linear structural equation models is identifiable as the minimum-trace DAG when the error variances are w…
Dimension-free Relaxation Times of Informed MCMC Samplers on Discrete Spaces
Hyunwoong Chang, Quan Zhou
Convergence analysis of Markov chain Monte Carlo methods in high-dimensional statistical applications is increasingly recognized. In this paper, we develop general mixing time boun…
Rapidly Mixing Multiple-try Metropolis Algorithms for Model Selection Problems
Hyunwoong Chang, Changwoo J. Lee, Zhao Tang Luo +2
The multiple-try Metropolis (MTM) algorithm is an extension of the Metropolis-Hastings (MH) algorithm by selecting the proposed state among multiple trials according to some weight…
Order-based Structure Learning without Score Equivalence
Hyunwoong Chang, James Cai, Quan Zhou
We propose an empirical Bayes formulation of the structure learning problem, where the prior specification assumes that all node variables have the same error variance, an assumpti…
Complexity analysis of Bayesian learning of high-dimensional DAG models and their equivalence classes
Quan Zhou, Hyunwoong Chang
Structure learning via MCMC sampling is known to be very challenging because of the enormous search space and the existence of Markov equivalent DAGs. Theoretical results on the mi…
A non-convex regularization approach for stable estimation of loss development factors
Himchan Jeong, Hyunwoong Chang, Emiliano A. Valdez
In this article, we apply non-convex regularization methods in order to obtain stable estimation of loss development factors in insurance claims reserving. Among the non-convex reg…