What question did this study set out to answer?

To provide a unified theoretical framework connecting PAC-Bayesian analysis and information-theoretic principles for deriving generalization bounds in machine learning.

January 23, 2025Foundations and Trends® in Machine Learning

Generalization Bounds: Perspectives from Information Theory and PAC-Bayes

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To provide a unified theoretical framework connecting PAC-Bayesian analysis and information-theoretic principles for deriving generalization bounds in machine learning.
Synthesized theoretical literature bridging PAC-Bayesian frameworks with information-theoretic measures of generalization.
Analyzed common proof architectures, analytical formulations of algorithmic information complexity, and conditional mutual information (CMI) frameworks applicable to deep neural networks.
Demonstrated that PAC-Bayesian and information-theoretic generalization bounds share fundamentally modular proof structures and overlapping mathematical mechanisms.
Clarified how conditional mutual information and information complexity concepts characterize the generalization capability and stability of deep learning models.

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Implication

Monograph demonstrates unified mathematical links between PAC-Bayes and information theory in machine learning algorithms, highlighting modular proof techniques for deep learning bounds.

Cite This Study

Hellström et al. (2025) studied this question.

synapsesocial.com/papers/6a1047b1e1a472cb5efc9cd8 https://doi.org/10.1561/2200000112

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Implication

Monograph demonstrates unified mathematical links between PAC-Bayes and information theory in machine learning algorithms, highlighting modular proof techniques for deep learning bounds.

Generalization Bounds: Perspectives from Information Theory and PAC-Bayes

Key Points

Discussion

Implication

Cite This Study

Generalization Bounds: Perspectives from Information Theory and PAC-Bayes

Key Points

Discussion

Implication

Cite This Study

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