16 citations · 48 across the 30 of their papers we have counts for
31 papers
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…
HOMER: Huber-of-Means for Efficient and Robust Estimation in Hilbert Spaces
Kisung You, Boram Cho
Heavy tails weaken high-confidence control for the empirical mean. Geometric median-of-means (MOM) also lacks a threshold that moves toward mean efficiency. We propose \emph{HOMER}…
Heat-Kernel Entropy Profiles and Geometric Effective Sample Size for Weighted Measures on Manifolds
Kisung You, Boram Cho
Weighted empirical measures on compact manifolds appear in importance sampling, particle approximations, posterior summaries, quadrature, and representation learning. Ordinary effe…
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…
Fast Computation of Free-Support Wasserstein Medians
Kisung You, Mauro Giuffré, Dennis Shung
The Wasserstein median is a robust alternative to the Wasserstein barycenter for averaging probability measures, but exact empirical computation can be expensive. A natural metric-…
Barycentric Projections of Optimal Transport Plans on Riemannian Manifolds
Kisung You
Optimal transport couplings are probabilistic objects, while many learning pipelines require deterministic maps. In Euclidean space, barycentric projection converts a coupling into…
Density Evolution: A Multiscale View of Density Estimation
Kisung You
Density estimation is often presented as a choice among parametric summaries, finite mixtures, and nonparametric smoothers. This review argues for a complementary view: a data set…
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…
Scale selection for geometric medians on product manifolds
Kisung You
Geometric medians on product manifolds are sensitive to the relative scaling of factor metrics because the median objective couples the factors rather than separating them. We stud…
Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy
Kisung You
Effective sample size is a standard summary of Markov chain Monte Carlo output, but it is usually attached to scalar or Euclidean summaries chosen by the analyst. For manifold-valu…
Profile Likelihood Inference for Anisotropic Hyperbolic Wrapped Normal Models on Hyperbolic Space
Kisung You
We study likelihood-based inference for the anisotropic hyperbolic wrapped normal distribution on standard hyperbolic space. The model has a manifold-valued location parameter and…
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…
Constant Metric Scaling in Riemannian Computation
Kisung You
Constant rescaling of a Riemannian metric appears in many computational settings, often through a global scale parameter that is introduced either explicitly or implicitly. Althoug…
A Particle-Flow Algorithm for Free-Support Wasserstein Barycenters
Kisung You
The Wasserstein barycenter extends the Euclidean mean to the space of probability measures by minimizing the weighted sum of squared 2-Wasserstein distances. We develop a free-supp…
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…
Scalable Geometric Learning with Correlation-Based Functional Brain Networks
Kisung You, Yelim Lee, Hae-Jeong Park
The correlation matrix is a central representation of functional brain networks in neuroimaging. Traditional analyses often treat pairwise interactions independently in a Euclidean…
Performance guarantees of light-cone variational quantum algorithms for the maximum cut problem
Xiaoyang Wang, Yuexin Su, Tongyang Li
Variational quantum algorithms (VQAs) are promising to demonstrate the advantage of near-term quantum computing over classical computing in practical applications, such as the maxi…
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…