PAC-Bayes Analysis for Linear Models: A Theoretical Advancement

This research explores PAC-Bayes bounds within the context of multivariate linear regression and linear autoencoders, suggesting potential improvements in understanding model generalization. The use of PAC-Bayes provides a valuable framework for analyzing the performance guarantees of these fundamental machine learning models.

• •Applies PAC-Bayes theory to analyze linear models.
• •Focuses on multivariate linear regression and autoencoders.
• •Potentially provides new insights into generalization bounds.