2 papers
stat.ME2026
Geometry-preserving and interpretable dimension reduction for compositional data
Junyoung Park, Cheolwoo Park, Jeongyoun Ahn
High-dimensional compositional data pose unique statistical challenges due to the simplex constraint and excess zeros. While dimension reduction is indispensable for analyzing such…
stat.ML2026
Optimal differentially private kernel learning with random projection
Bonwoo Lee, Cheolwoo Park, Jeongyoun Ahn
Differential privacy has become a cornerstone in the development of privacy-preserving learning algorithms. This work addresses optimizing differentially private kernel learning wi…