Reliable Risk Estimation with Kernel Mean Matching under Dependent Sampling
Kernel Mean Matching (KMM) estimates risk under covariate shift by reweighting labelled source samples so that their kernel mean embedding matches that of unlabeled target samples. Existing finite-sample guarantees are usually stated for independent samples, although many applications involve temporal, spatial, or clus...
A. Zaytsev, Diana Koldasbayeva, Egor Serov
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