3 papers
math.ST2026
Minimax Wasserstein Testing under Differential Privacy
Bonwoo Lee, Ilmun Kim, Cheolwoo Park
We study Wasserstein- goodness-of-fit and two-sample testing under differential privacy. For general compact metric spaces, we establish upper and lower bounds on the minimax se…
cs.LG2025
Locally Optimal Private Sampling: Beyond the Global Minimax
Hrad Ghoukasian, Bonwoo Lee, Shahab Asoodeh
We study the problem of sampling from a distribution under local differential privacy (LDP). Given a private distribution , the goal is to generate a single samp…
stat.ML2025
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…