arXiv:2605.28679v1 Announce Type: new Abstract: We consider $L^2$-regularized linear (ridge) regression over a finite data sample $X$ with bounded covariance and linear prediction targets $y$ with additive isotropic noise of finite variance. We present an iterative procedure to compute the optimal regularization strength numerically from the generative parameters in the fixed-$X$ setting and prove its convergence at limited noise levels. Our experimental evaluation over synthetic data shows that the proposed procedure combined with sample-based parameter estimates attains near-optimal random-$
This research provides a refined method for optimizing regularization in linear regression, addressing ongoing efforts to improve machine learning model stability and performance.
Improved regularization techniques can lead to more robust and accurate AI models, reducing computational waste and enhancing reliability in various applications.
The proposed iterative procedure offers a more precise numerical approach to optimal regularization, potentially leading to marginal but consistent gains in model performance.
- · AI researchers
- · Machine learning practitioners
- · Data scientists
This research will be integrated into machine learning libraries and algorithms, offering marginal improvements in model training.
Slightly more efficient and reliable AI model development could accelerate progress in specific domains, albeit incrementally.
Broader adoption of such techniques contributes to the overall maturation and industrialization of AI development.
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