Research

Research Interests

• High-dimensional inference

• Nonparametric inference

• Asymptotic theory

• Information theory

• Kernel methods

• Minimax testing

Publications and Preprints

• Minimax Optimal Two-Sample Testing under Local Differential Privacy

  • Jongmin Mun, Seungwoo Kwak and Ilmun Kim

• General Frameworks for Conditional Two-Sample Testing

  • Seongchan Lee, Suman Cha and Ilmun Kim

• Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier Features

  • Ikjun Choi and Ilmun Kim

• Robust Kernel Hypothesis Testing under Data Corruption

  • Antonin Schrab* and Ilmun Kim* (equal contribution)

• Enhancing Sufficient Dimension Reduction via Hellinger Correlation

  • Seungbeom Hong, Ilmun Kim and Jun Song

  • ICML (2024)

• Semi-Supervised U-statistics

  • Ilmun Kim, Larry Wasserman, Sivaraman Balakrishnan and Matey Neykov

• Differentially Private Permutation Tests: Applications to Kernel Methods

  • Ilmun Kim* and Antonin Schrab* (equal contribution)

• Nearly Minimax Optimal Wasserstein Conditional Independence Testing

  • Matey Neykov, Larry Wasserman, Ilmun Kim and Sivaraman Balakrishnan

  • Information and Inference: A Journal of the IMA (accepted)

• Conditional Independence Testing for Discrete Distributions: Beyond Chi-squared and G-tests

  • Ilmun Kim, Matey Neykov, Sivaraman Balakrishnan and Larry Wasserman

  • Electronic Journal of Statistics (accepted)

• A Permutation-Free Kernel Independence Test

  • Shubhanshu Shekhar, Ilmun Kim and Aaditya Ramdas

  • Journal of Machine Learning Research (2023)

• Averaging p-values under exchangeability

  • Woohyun Choi and Ilmun Kim

  • Statistics & Probability Letters (2023)

• The Projected Covariance Measure for assumption-lean variable significance testing

  • Anton Rask Lundborg, Ilmun Kim, Rajen D. Shah and Richard J. Samworth

  • Annals of Statistics (accepted)

• A Permutation-Free Kernel Two-Sample Test

  • Shubhanshu Shekhar, Ilmun Kim and Aaditya Ramdas

  • NeurIPS (2022)

  • Accepted for oral presentation

• Comments on “Testing Conditional Independence of Discrete Distributions”

  • Ilmun Kim

• Computationally Efficient Aggregated Kernel Tests using Incomplete U-statistics

  • Antonin Schrab, Ilmun Kim, Benjamin Guedj and Arthur Gretton

  • NeurIPS (2022)

• Local permutation tests for conditional independence

  • Ilmun Kim, Matey Neykov, Sivaraman Balakrishnan and Larry Wasserman

  • Annals of Statistics (2022)

  • Selected as Outstanding Paper by the National Research Foundation of Korea (2023)

• Comparing multiple latent space embeddings using topological analysis

  • Kisung You, Ilmun Kim, Ick Hoon Jin, Minjeong Jeon and Dennis Shung

• MMD Aggregated Two-Sample Test

  • Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj and Arthur Gretton

  • Journal of Machine Learning Research (2023)

• Dimension-agnostic inference using cross U-statistics

  • Ilmun Kim and Aaditya Ramdas

  • Bernoulli (2024)

• Minimax optimality of permutation tests

  • Ilmun Kim, Sivaraman Balakrishnan and Larry Wasserman

  • Annals of Statistics (2022)

• Randomized tests for high-dimensional regression: A more efficient and powerful solution

  • Yue Li, Ilmun Kim and Yuting Wei

  • NeurIPS (2020)

• Classification accuracy as a proxy for two-sample testing

  • Ilmun Kim*, Aaditya Ramdas*, Aarti Singh and Larry Wasserman

  • Annals of Statistics (2021)

  • Winner of Statistical Learning and Data Science Student Paper Award, JSM 2020

• Comparing a Large Number of Multivariate Distributions

  • Ilmun Kim

  • Bernoulli (2021)

• Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations

  • Niccolò Dalmasso, Ann B. Lee, Rafael Izbicki, Taylor Pospisil, Ilmun Kim and Chieh-An Lin

  • AISTATS (2020)

• Robust Multivariate Nonparametric Tests via Projection-Averaging

  • Ilmun Kim, Sivaraman Balakrishnan and Larry Wasserman

  • Annals of Statistics (2021)

• Global and Local Two-Sample Tests via Regression

  • Ilmun Kim, Ann B. Lee and Jing Lei

  • Electronic Journal of Statistics (2019)

• Multinomial goodness-of-fit based on U-statistics: High-dimensional asymptotic and minimax optimality

  • Ilmun Kim

  • Journal of Statistical Planning and Inference (2019)

• Likelihood ratio tests for multivariate normality

  • Ilmun Kim and Sangun Park

  • Communications in Statistics-Theory and Methods (2018)

• General Cumulative Kullback–Leibler Information

  • Sangun Park, Hadi Alizadeh Noughabi and Ilmun Kim

  • Communications in Statistics-Theory and Methods (2018)

• Local two-sample testing: a new tool for analysing high-dimensional astronomical data

  • Peter E. Freeman, Ilmun Kim and Ann B. Lee

  • Monthly Notices of the Royal Astronomical Society (2017)

• Cumulative ratio information based on general cumulative entropy

  • Sangun Park and Ilmun Kim

  • Journal of Statistical Computation and Simulation (2017)

• Kullback–Leibler Information of Consecutive Order Statistics

  • Ilmun Kim and Sangun Park

  • Communications for Statistical Applications and Methods (2015)

• On cumulative residual entropy of order statistics

  • Sangun Park and Ilmun Kim

  • Statistics & Probability Letters (2014)