Covariate Shift
A common form of distribution shift in which the marginal distribution of the input features changes while the conditional distribution of the label given the input remains unchanged.
Timeline (last 90 days)
A recent study extends BCP to the covariate shift setting, preserving its theoretical guarantees through importance weighting.
When covariate shift is present, the input distribution P(X) changes, but the conditional distribution P(Y|X) remains unchanged.
Directly applying BCP under covariate shift leads to bias in the miscoverage rate estimate.
The proposed method improves the overall miscoverage rate estimation performance of BCP under covariate shift.
All Facts (4)
When covariate shift is present, the input distribution P(X) changes, but the conditional distribution P(Y|X) remains unchanged.
50%UnverifiedDirectly applying BCP under covariate shift leads to bias in the miscoverage rate estimate.
50%UnverifiedThe proposed method improves the overall miscoverage rate estimation performance of BCP under covariate shift.
50%UnverifiedA recent study extends BCP to the covariate shift setting, preserving its theoretical guarantees through importance weighting.
50%