16 citations · 48 across the 30 of their papers we have counts for
14 papers · 1 filter
Hyperbolic Latent Position Models for Hierarchical Bipartite Data
Kisung You
Binary bipartite data often combine row and column heterogeneity with hierarchical interaction, as when students answer exercises organized by prerequisites or legislators vote on…
Exact Likelihood and Sampling for Riemannian Gaussian Distributions on Correlation Matrices
Kisung You
Correlation matrices arise when marginal scales are removed from covariance matrices, yet a normalized likelihood must account for both quotient distance and quotient volume. We pr…
Geometric Information Decomposition for Weighted Empirical Measures on the Sphere
Kisung You, Boram Cho
Weighted observations on the unit sphere arise in importance sampling, quadrature, and attention-weighted embeddings. Directional uncertainty is often summarized through a von Mise…
Scale-Calibrated Median-of-Means for Robust Distributed Principal Component Analysis
Kisung You
Distributed principal component analysis (PCA) produces node-level estimates of both a mean vector and a principal subspace. Robustly aggregating these heterogeneous objects requir…
Finite Mixture Modeling with Riemannian Gaussian Distributions on Hyperbolic Space
Kisung You
Hyperbolic space is increasingly used for hierarchical, tree-like, and network-structured data, but likelihood-based density modeling on hyperbolic space remains relatively limited…
On Heterogeneity in Wasserstein Space
Kisung You
Data represented by probability measures arise as empirical distributions, posterior distributions, and feature-based representations of complex objects. We study heterogeneity in…
Geometric medians on product manifolds
Jiewon Park, Kisung You
Product manifolds arise when heterogeneous geometric variables are jointly observed. While the Fréchet mean on Riemannian manifolds separates cleanly across factors, the canonical…
PCA, SVD, and Centering of Data
Donggun Kim, Kisung You
The research detailed in this paper scrutinizes Principal Component Analysis (PCA), a seminal method employed in statistics and machine learning for the purpose of reducing data di…
On the spherical Laplace distribution
Kisung You, Dennis Shung
The von Mises-Fisher (vMF) distribution has long been a mainstay for inference with data on the unit hypersphere in directional statistics. The performance of statistical inference…
On the Wasserstein median of probability measures
Kisung You, Dennis Shung, Mauro Giuffrè
The primary choice to summarize a finite collection of random objects is by using measures of central tendency, such as mean and median. In the field of optimal transport, the Wass…
Comparing multiple latent space embeddings using topological analysis
Kisung You, Ilmun Kim, Ick Hoon Jin +2
The latent space model is one of the well-known methods for statistical inference of network data. While the model has been much studied for a single network, it has not attracted…
Bayesian Optimal Two-sample Tests in High-dimension
Kyoungjae Lee, Kisung You, Lizhen Lin
We propose optimal Bayesian two-sample tests for testing equality of high-dimensional mean vectors and covariance matrices between two populations. In many applications including g…
Parameter Estimation and Model-Based Clustering with Spherical Normal Distribution on the Unit Hypersphere
Kisung You
In directional statistics, the von Mises-Fisher (vMF) distribution is one of the most basic and popular probability distributions for data on the unit hypersphere. Recently, the sp…
Data transforming augmentation for heteroscedastic models
Hyungsuk Tak, Kisung You, Sujit K. Ghosh +2
Data augmentation (DA) turns seemingly intractable computational problems into simple ones by augmenting latent missing data. In addition to computational simplicity, it is now wel…