Minimax Lower Bounds of Kernel Discrepancy Estimation: MMD, HSIC, KSD

Over the past 20 years, kernel discrepancies have been leveraged as a highly powerful tool for quantifying the disagreement of distributions, with numerous successful applications in two-sample, goodness-of-fit, and independence testing, among others. Their fastest estimators are known to converge a...