13 citations · 85 across the 25 of their papers we have counts for
26 papers
A Two-Sample Test on Weighted Persistence Intensity Functions in Topological Data Analysis
Yeongung Han, Ilmun Kim, Jisu Kim
Persistence intensity functions provide interpretable and informative first-order summaries of random persistence diagram distributions. We study two-sample testing for equality of…
When Do Generalized Permutation Tests Achieve Optimal Power? A Dispersion Characterization
Yongmin Kim, Ilmun Kim
We study generalized Monte Carlo permutation tests under a non-uniform distribution on permutations. Focusing on the difference-in-means statistic, we introduce two scalar dispersi…
A Semi-Supervised Kernel Two-Sample Test
Gyumin Lee, Shubhanshu Shekhar, Ilmun Kim
We consider the problem of two-sample testing in a semi-supervised setting with abundant unlabeled covariate data. Standard two-sample tests neglect covariate information, which ha…
Multi-LLM Adaptive Conformal Inference for Reliable LLM Responses
Kangjun Noh, Seongchan Lee, Ilmun Kim +1
Ensuring factuality is essential for the safe use of Large Language Models (LLMs) in high-stakes domains such as medicine and law. Conformal inference provides distribution-free gu…
Transfer Learning for Benign Overfitting in High-Dimensional Linear Regression
Yeichan Kim, Ilmun Kim, Seyoung Park
Transfer learning is a key component of modern machine learning, enhancing the performance of target tasks by leveraging diverse data sources. Simultaneously, overparameterized mod…
Locally minimax optimal confidence sets for the best model
Ilmun Kim, Aaditya Ramdas
This paper tackles a fundamental inference problem: given observations from a distribution over with unknown mean , we must form a confidence s…
Semi-Supervised U-statistics
Ilmun Kim, Larry Wasserman, Sivaraman Balakrishnan +1
Semi-supervised datasets are ubiquitous across diverse domains where obtaining fully labeled data is costly or time-consuming. The prevalence of such datasets has consistently driv…
Nearly Minimax Optimal Wasserstein Conditional Independence Testing
Matey Neykov, Larry Wasserman, Ilmun Kim +1
This paper is concerned with minimax conditional independence testing. In contrast to some previous works on the topic, which use the total variation distance to separate the null…
Conditional Independence Testing for Discrete Distributions: Beyond - and -tests
Ilmun Kim, Matey Neykov, Sivaraman Balakrishnan +1
This paper is concerned with the problem of conditional independence testing for discrete data. In recent years, researchers have shed new light on this fundamental problem, emphas…
A Permutation-Free Kernel Independence Test
Shubhanshu Shekhar, Ilmun Kim, Aaditya Ramdas
In nonparametric independence testing, we observe i.i.d.\ data , where lie in any general spaces, and we wish to test…
A Permutation-free Kernel Two-Sample Test
Shubhanshu Shekhar, Ilmun Kim, Aaditya Ramdas
The kernel Maximum Mean Discrepancy~(MMD) is a popular multivariate distance metric between distributions that has found utility in two-sample testing. The usual kernel-MMD test st…
The Projected Covariance Measure for assumption-lean variable significance testing
Anton Rask Lundborg, Ilmun Kim, Rajen D. Shah +1
Testing the significance of a variable or group of variables for predicting a response , given additional covariates , is a ubiquitous task in statistics. A simple but co…
Comparing multiple latent space embeddings using topological analysis
Kisung You, Ilmun Kim, Ick Hoon Jin +2
The latent space model is one of the well-known methods for statistical inference of network data. While the model has been much studied for a single network, it has not attracted…
Comments on "Testing Conditional Independence of Discrete Distributions"
Ilmun Kim
In this short note, we identify and address an error in the proof of Theorem 1.3 in Canonne et al. (2018), a recent breakthrough in conditional independence testing. After correcti…
Efficient Aggregated Kernel Tests using Incomplete -statistics
Antonin Schrab, Ilmun Kim, Benjamin Guedj +1
We propose a series of computationally efficient nonparametric tests for the two-sample, independence, and goodness-of-fit problems, using the Maximum Mean Discrepancy (MMD), Hilbe…
Local permutation tests for conditional independence
Ilmun Kim, Matey Neykov, Sivaraman Balakrishnan +1
In this paper, we investigate local permutation tests for testing conditional independence between two random vectors and given . The local permutation test determines t…
MMD Aggregated Two-Sample Test
Antonin Schrab, Ilmun Kim, Mélisande Albert +3
We propose two novel nonparametric two-sample kernel tests based on the Maximum Mean Discrepancy (MMD). First, for a fixed kernel, we construct an MMD test using either permutation…
Dimension-agnostic inference using cross U-statistics
Ilmun Kim, Aaditya Ramdas
Classical asymptotic theory for statistical inference usually involves calibrating a statistic by fixing the dimension while letting the sample size increase to infinity. R…
Randomized tests for high-dimensional regression: A more efficient and powerful solution
Yue Li, Ilmun Kim, Yuting Wei
We investigate the problem of testing the global null in the high-dimensional regression models when the feature dimension grows proportionally to the number of observations $n…
Minimax optimality of permutation tests
Ilmun Kim, Sivaraman Balakrishnan, Larry Wasserman
Permutation tests are widely used in statistics, providing a finite-sample guarantee on the type I error rate whenever the distribution of the samples under the null hypothesis is…
Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations
Niccolò Dalmasso, Ann B. Lee, Rafael Izbicki +3
Complex phenomena in engineering and the sciences are often modeled with computationally intensive feed-forward simulations for which a tractable analytic likelihood does not exist…
Comparing a Large Number of Multivariate Distributions
Ilmun Kim
In this paper, we propose a test for the equality of multiple distributions based on kernel mean embeddings. Our framework provides a flexible way to handle multivariate or even hi…
Multinomial Goodness-of-Fit Based on U-Statistics: High-Dimensional Asymptotic and Minimax Optimality
Ilmun Kim
We consider multinomial goodness-of-fit tests in the high-dimensional regime where the number of bins increases with the sample size. In this regime, Pearson's chi-squared test can…
Global and Local Two-Sample Tests via Regression
Ilmun Kim, Ann B. Lee, Jing Lei
Two-sample testing is a fundamental problem in statistics. Despite its long history, there has been renewed interest in this problem with the advent of high-dimensional and complex…