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Found 3 papers, 1 papers with code
Minimax Optimal Goodness-of-Fit Testing with Kernel Stein Discrepancy
We explore the minimax optimality of goodness-of-fit tests on general domains using the kernelized Stein discrepancy (KSD).
Spectral Regularized Kernel Goodness-of-Fit Tests
Maximum mean discrepancy (MMD) has enjoyed a lot of success in many machine learning and statistical applications, including non-parametric hypothesis testing, because of its ability to handle non-Euclidean data.
Spectral Regularized Kernel Two-Sample Tests
First, we show the popular MMD (maximum mean discrepancy) two-sample test to be not optimal in terms of the separation boundary measured in Hellinger distance.
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