Researchers have developed a new semi-supervised regression method designed for scenarios with abundant noisy proxy covariates and scarce task-specific labels. The proposed two-stage estimator learns kernel eigenfeatures from all covariates and then fits a ridge predictor using the limited labeled data. Theoretical bounds show this approach can achieve efficient learning rates, particularly when unlabeled proxy data is plentiful and perturbation is managed, with distribution regression identified as a special case. AI
IMPACT Introduces a novel approach to semi-supervised learning that could improve model performance in data-scarce environments.
RANK_REASON The cluster contains a research paper published on arXiv detailing a new machine learning methodology.
- arXiv
- Semi-Supervised Learning with Noisy Proxy Covariates: Generalization Bounds and Distribution Regression
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