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Analysis of Kernel Mean Matching under Covariate Shift
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In real supervised learning scenarios, it is not uncommon that the training and test sample follow different probability distributions, thus rendering the necessity to correct the sampling bias. Focusing on a particular covariate shift problem, we derive high probability confidence bounds for the kernel mean matching (KMM) estimator, whose convergence rate turns out to depend on some regularity measure of the regression function and also on some capacity measure of the kernel. By comparing KMM with the natural plug-in estimator, we establish the superiority of the former hence provide concrete evidence/understanding to the effectiveness of KMM under covariate shift.
Forward citations
Cited by 2 Pith papers
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Nonparametric Goodness-of-fit Testing under Covariate Shift
Truncated importance-weighted kernel ridge regression with multiplier bootstrap yields valid L2(Q) confidence balls for nonparametric goodness-of-fit under covariate shift.
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Minimax Optimal Two-Stage Algorithm For Moment Estimation Under Covariate Shift
A two-stage importance-weighted estimator is claimed to achieve the minimax rate for moment estimation under covariate shift, but the lower-bound proof is flawed.
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