A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization. (arXiv:2111.02355v2 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2111.02355

Covariate-shift generalization, a typical case in out-of-distribution (OOD)
generalization, requires a good performance on the unknown test distribution,
which varies from the accessible training distribution in the form of covariate
shift. Recently, independence-driven importance weighting algorithms in stable
learning literature have shown empirical effectiveness to deal with
covariate-shift generalization on several learning models, including regression
algorithms and deep neural networks, while their theoretical analyses are
missing. In this paper, we theoretically prove the effectiveness of such
algorithms by explaining them …

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