1 citations · 2 across the 13 of their papers we have counts for
14 papers
Joint estimation of high-dimensional spiked covariance matrices via a partially shared subspace
Changwon Yoon, Minwoo Kim, Sungkyu Jung +1
Statistical analysis of high-dimensional data is often hampered by limited sample sizes, yet auxiliary datasets from related sources are often readily available. When two such data…
Enhancing Differentially Private Mechanisms via Empirical Bayes
Minwoo Kim, Junyong Park, Sungkyu Jung
Differential privacy (DP) has become the gold standard for ensuring the privacy protection of machine learning and statistical algorithms in recent decades. A plethora of algorithm…
FAST-DIPS: Adjoint-Free Analytic Steps and Hard-Constrained Likelihood Correction for Diffusion-Prior Inverse Problems
Minwoo Kim, Seunghyeok Shin, Hongki Lim
Training-free diffusion priors enable inverse-problem solvers without retraining, but for nonlinear forward operators data consistency often relies on repeated derivatives or inner…
A rigorous hybridization of variational quantum eigensolver with classical neural network
Minwoo James Kim, Kyoung Keun Park, Kyungmin Lee +2
Combining variational quantum process with classical neural learning offers a flexible route to improve ground-state estimation. We establish an integrated framework that connects…
SURE Guided Posterior Sampling: Trajectory Correction for Diffusion-Based Inverse Problems
Minwoo Kim, Hongki Lim
Diffusion models have emerged as powerful learned priors for solving inverse problems. However, current iterative solving approaches which alternate between diffusion sampling and…
Robust and Differentially Private Principal Component Analysis
Minwoo Kim, Sungkyu Jung
Recent advances have sparked significant interest in the development of privacy-preserving Principal Component Analysis (PCA). However, many existing approaches rely on restrictive…
A scalable quantum-neural hybrid variational algorithm for ground state estimation
Minwoo Kim, Kyoung Keun Park, Uihwan Jeong +2
We propose the unitary variational quantum-neural hybrid eigensolver (U-VQNHE), which improves upon the original VQNHE by enforcing unitary neural transformations. The non-unitary…
Quantum Linear Multistep Method for Using a Quantum Oracle with Differential Equations
Kyoung Keun Park, Kwangyeul Choi, Minwoo Kim +2
Differential equations are a crucial mathematical tool used in a wide range of applications. If the solution to an initial value problem (IVP) can be transformed into an oracle, it…
Modeling High-Resolution Spatio-Temporal Wind with Deep Echo State Networks and Stochastic Partial Differential Equations
Kesen Wang, Minwoo Kim, Stefano Castruccio +1
In the past decades, clean and renewable energy has gained increasing attention due to a global effort on carbon footprint reduction. In particular, Saudi Arabia is gradually shift…
MPCR: Multi-Precision Computations Package in R
Mary Lai O. Salvana, Sameh Abdulah, Minwoo Kim +3
In the early days of computing, severe memory constraints made it necessary to use lower floating-point precision. As hardware capabilities have advanced, modern systems, particula…
A Neural Network-Based Approach to Normality Testing for Dependent Data
Minwoo Kim, Marc G Genton, Raphael Huser +1
There is a wide availability of methods for testing normality under the assumption of independent and identically distributed data. When data are dependent in space and/or time, ho…
Differentially Private Multivariate Statistics with an Application to Contingency Table Analysis
Minwoo Kim, Jonghyeok Lee, Seung Woo Kwak +1
Differential privacy (DP) has become a rigorous central concept for privacy protection in the past decade. We use Gaussian differential privacy (GDP) in gauging the level of privac…
Variable selection and basis learning for ordinal classification
Minwoo Kim, Sangil Han, Jeongyoun Ahn +1
We propose a method for variable selection and basis learning for high-dimensional classification with ordinal responses. The proposed method extends sparse multiclass linear discr…
Variational Bayes algorithm and posterior consistency of Ising model parameter estimation
Minwoo Kim, Shrijita Bhattacharya, Tapabrata Maiti
Ising models originated in statistical physics and are widely used in modeling spatial data and computer vision problems. However, statistical inference of this model remains chall…