Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations. - Niccolò Dalmasso; Ann B. Lee; Rafael Izbicki; Taylor Pospisil; Ilmun Kim; Chieh-An Lin (AISTATS, 2020)

Randomized tests for high-dimensional regression: A more efficient and powerful solution. - Yue Li; Ilmun Kim; Yuting Wei 001 (NeurIPS, 2020)

Efficient Aggregated Kernel Tests using Incomplete $U$-statistics. - Antonin Schrab; Ilmun Kim; Benjamin Guedj; Arthur Gretton (NeurIPS, 2022)

A permutation-free kernel two-sample test. - Shubhanshu Shekhar; Ilmun Kim; Aaditya Ramdas (NeurIPS, 2022)

Comments on "Testing Conditional Independence of Discrete Distributions". - Ilmun Kim (CoRR, 2022)

Differentially Private Permutation Tests: Applications to Kernel Methods. - Ilmun Kim; Antonin Schrab (CoRR, 2023)

MMD Aggregated Two-Sample Test. - Antonin Schrab; Ilmun Kim; Mélisande Albert; Béatrice Laurent; Benjamin Guedj; Arthur Gretton (J. Mach. Learn. Res., 2023)

A Permutation-Free Kernel Independence Test. - Shubhanshu Shekhar; Ilmun Kim; Aaditya Ramdas (J. Mach. Learn. Res., 2023)

Enhancing Sufficient Dimension Reduction via Hellinger Correlation. - Seungbeom Hong; Ilmun Kim; Jun Song (ICML, 2024)

Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier Features. - Ikjun Choi; Ilmun Kim (CoRR, 2024)

General Frameworks for Conditional Two-Sample Testing. - Seongchan Lee; Suman Cha; Ilmun Kim (CoRR, 2024)

Minimax Optimal Two-Sample Testing under Local Differential Privacy. - Jongmin Mun; Seungwoo Kwak; Ilmun Kim (CoRR, 2024)

Robust Kernel Hypothesis Testing under Data Corruption. - Antonin Schrab; Ilmun Kim (AISTATS, 2025)