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This is a Quantitative Study study.

October 20, 2025Open Access

A Simplified Analysis of SGD for Linear Regression with Weight Averaging

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Analysis reveals bias and variance bounds for SGD in linear regression, suggesting improved optimization techniques.

This analysis recovers bias and variance bounds for SGD optimization in linear regression.
The study demonstrates that linear algebra tools can simplify complex operator manipulations on PSD matrices.
Application of findings could enhance learning rate scheduling and mini-batching in machine learning models.
Improving the accessibility of SGD analysis can facilitate deeper understanding and better optimization in practice.

Meterez et al. (2025) studied this question.

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