5 papers · 2 filters
Minimax Optimal Two-Sample Testing under Local Differential Privacy
Jongmin Mun, Seungwoo Kwak, Ilmun Kim
We explore the trade-off between privacy and statistical utility in private two-sample testing under local differential privacy (LDP) for both multinomial and continuous data. We b…
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…
Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier Features
Ikjun Choi, Ilmun Kim
Recent years have seen a surge in methods for two-sample testing, among which the Maximum Mean Discrepancy (MMD) test has emerged as an effective tool for handling complex and high…
Enhancing Sufficient Dimension Reduction via Hellinger Correlation
Seungbeom Hong, Ilmun Kim, Jun Song
In this work, we develop a new theory and method for sufficient dimension reduction (SDR) in single-index models, where SDR is a sub-field of supervised dimension reduction based o…
Robust Kernel Hypothesis Testing under Data Corruption
Antonin Schrab, Ilmun Kim
We propose a general method for constructing robust permutation tests under data corruption. The proposed tests effectively control the non-asymptotic type I error under data corru…