3 papers
stat.CO2026
More Permutations Do Not Always Increase Power: Non-monotonicity in Monte Carlo Permutation Tests
Suman Cha, Seongchan Lee, Antonin Schrab +1
Monte Carlo permutation tests are a cornerstone of valid, model-free statistical inference. A widely held practical intuition is that increasing the number of sampled permutations…
stat.ML2025
Learning Majority-to-Minority Transformations with MMD and Triplet Loss for Imbalanced Classification
Suman Cha, Hyunjoong Kim
Class imbalance in supervised classification often degrades model performance by biasing predictions toward the majority class, particularly in critical applications such as medica…
stat.ML2024
General Frameworks for Conditional Two-Sample Testing
Seongchan Lee, Suman Cha, Ilmun Kim
We study the problem of conditional two-sample testing, which aims to determine whether two populations have the same distribution after accounting for confounding factors. This pr…