Nyström-approximated importance-weighted kernel ridge regression achieves the same optimal excess-risk rates as the full method under covariate shift, with sublinear time and memory costs.
(2015) it’s easy to show that bΣ1/2 wλ V(V ∗ bΣwλV) −1V ∗ bΣ1/2 wλ 2 = bΣ1/2 wλ V(V ∗ bΣwλV) −1V ∗ bΣ1/2 wλ , and therefore the only possible values forA 2 are0and1
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Computational Efficiency under Covariate Shift in Kernel Ridge Regression
Nyström-approximated importance-weighted kernel ridge regression achieves the same optimal excess-risk rates as the full method under covariate shift, with sublinear time and memory costs.