Semi-Supervised Learning with Noisy Proxy Covariates: Generalization Bounds and Distribution Regression
arXiv:2606.00512v1 Announce Type: new Abstract: In many modern machine learning pipelines, abundant pretrained representations serve as noisy proxy covariates, while task-specific labels remain scarce. We study semi-supervised regression in this setting, and propose a simple two stage estimator that learns kernel eigenfeatures from all proxy covariates and fits a ridge predictor on labeled data. We derive finite sample bounds showing that fast labeled sample rates are recovered when proxy perturbation is controlled and unlabeled proxy covariates are sufficiently abundant. We also show that dis
This research addresses the growing challenge in machine learning of leveraging abundant pretrained models when task-specific data is scarce, a common scenario in large-scale AI deployment.
Improved semi-supervised learning techniques for noisy, proxy covariates could significantly enhance the efficiency and performance of AI systems, particularly those relying on vast, loosely related data sets, leading to more robust and data-efficient models.
The development of more effective methods for utilizing noisy proxy covariates means AI models can achieve high performance with less labeled data, potentially accelerating AI development and deployment in data-scarce domains.
- · AI developers
- · Companies with large unlabeled datasets
- · Machine learning researchers
- · Companies reliant on solely supervised learning
- · Data labeling services
More efficient and generalizable AI models become possible with less reliance on extensive, costly labeled datasets.
This could accelerate the adoption of AI in industries where data labeling is difficult or prohibitively expensive, such as scientific research or proprietary enterprise data.
Advances in semi-supervised learning could contribute to the development of more adaptive and autonomous AI systems that require less human intervention for training and fine-tuning, impacting agentic systems.
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Read at arXiv cs.LG