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Found 7 papers, 6 papers with code
Practical Kernel Tests of Conditional Independence
We describe a data-efficient, kernel-based approach to statistical testing of conditional independence.
Differentially Private Permutation Tests: Applications to Kernel Methods
The proposed framework extends classical non-private permutation tests to private settings, maintaining both finite-sample validity and differential privacy in a rigorous manner.
MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting
We propose novel statistics which maximise the power of a two-sample test based on the Maximum Mean Discrepancy (MMD), by adapting over the set of kernels used in defining it.
Discussion of `Multiscale Fisher's Independence Test for Multivariate Dependence'
We discuss how MultiFIT, the Multiscale Fisher's Independence Test for Multivariate Dependence proposed by Gorsky and Ma (2022), compares to existing linear-time kernel tests based on the Hilbert-Schmidt independence criterion (HSIC).
Efficient Aggregated Kernel Tests using Incomplete $U$-statistics
We derive non-asymptotic uniform separation rates for MMDAggInc and HSICAggInc, and quantify exactly the trade-off between computational efficiency and the attainable rates: this result is novel for tests based on incomplete $U$-statistics, to our knowledge.
KSD Aggregated Goodness-of-fit Test
KSDAgg avoids splitting the data to perform kernel selection (which leads to a loss in test power), and rather maximises the test power over a collection of kernels.
MMD Aggregated Two-Sample Test
In practice, this parameter is unknown and, hence, the optimal MMD test with this particular kernel cannot be used.
