13 citations · 85 across the 25 of their papers we have counts for
7 papers · 1 filter
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…