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This is a Quantitative Study study.

October 3, 2025Open Access

Spectral Algorithms in Misspecified Regression: Convergence under Covariate Shift

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Authors

RLR. LiuUniversity of Wisconsin–Madison ZGZheng-Chu Guo

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Implication

This work establishes convergence rates for spectral algorithms under covariate shift, highlighting the role of importance weights in nonparametric regression.

Key Points

Minimax-optimal convergence rates are established for spectral algorithms under covariate shift conditions.
The incorporation of importance weights leads to improved performance in nonparametric regression models.
A novel truncation technique achieves near-optimal convergence rates despite unbounded importance weights.
The research extends classical kernel learning theory, addressing model misspecification in practical settings.

Cite This Study

Liu et al. (2025) studied this question.

synapsesocial.com/papers/68e02f46f0e39f13e7fa2e79 https://doi.org/10.48550/arxiv.2509.05106

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Implication

This work establishes convergence rates for spectral algorithms under covariate shift, highlighting the role of importance weights in nonparametric regression.

Spectral Algorithms in Misspecified Regression: Convergence under Covariate Shift

Discussion

Member takes

Implication

Key Points

Cite This Study

Spectral Algorithms in Misspecified Regression: Convergence under Covariate Shift

Discussion

Member takes

Implication

Key Points

Cite This Study

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