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Found 3 papers, 2 papers with code
Efficient Conditionally Invariant Representation Learning
The procedure requires just a single ridge regression from $Y$ to kernelized features of $Z$, which can be done in advance.
MMD-B-Fair: Learning Fair Representations with Statistical Testing
We introduce a method, MMD-B-Fair, to learn fair representations of data via kernel two-sample testing.
Unbiased estimators for the variance of MMD estimators
The maximum mean discrepancy (MMD) is a kernel-based distance between probability distributions useful in many applications (Gretton et al. 2012), bearing a simple estimator with pleasing computational and statistical properties.
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