#generalization-theory News & Analysis

2 articles tagged with #generalization-theory. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AIBullisharXiv – CS AI · Apr 107/10

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Path Regularization: A Near-Complete and Optimal Nonasymptotic Generalization Theory for Multilayer Neural Networks and Double Descent Phenomenon

Researchers propose a new nonasymptotic generalization theory for multilayer neural networks using path regularization, proving near-minimax optimal error bounds without requiring unbounded loss functions or infinite network dimensions. The theory notably explains the double descent phenomenon and solves an open problem in approximation theory for neural networks.

AINeutralarXiv – CS AI · Jun 56/10

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Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime

Researchers present a theoretical framework analyzing scaling laws for shallow neural networks in the feature learning regime, deriving phase diagrams that connect sample complexity and weight decay to risk exponents. The work bridges empirical observations in deep learning with rigorous mathematical analysis, establishing links between weight spectrum properties and generalization performance through matrix compressed sensing and LASSO theory.