16 citations · 48 across the 30 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…
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…
Shape-Preserving Dimensionality Reduction : An Algorithm and Measures of Topological Equivalence
Byeongsu Yu, Kisung You
We introduce a linear dimensionality reduction technique preserving topological features via persistent homology. The method is designed to find linear projection which preserv…
Rdimtools: An R package for Dimension Reduction and Intrinsic Dimension Estimation
Kisung You
Discovering patterns of the complex high-dimensional data is a long-standing problem. Dimension Reduction (DR) and Intrinsic Dimension Estimation (IDE) are two fundamental thematic…
Network Distance Based on Laplacian Flows on Graphs
Dianbin Bao, Kisung You, Lizhen Lin
Distance plays a fundamental role in measuring similarity between objects. Various visualization techniques and learning tasks in statistics and machine learning such as shape matc…