Unified Theory of Machine Learning : A Dynamical Theory of Stability, Generalization, and Solvability

Sandeep Kasukurthi

doi:10.5281/zenodo.18481240

February 4, 2026Open Access

Unified Theory of Machine Learning : A Dynamical Theory of Stability, Generalization, and Solvability

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Key Points

The aim is to develop a comprehensive theory explaining the interplay between stability, generalization, and solvability in machine learning models.
Theoretical framework development
Mathematical analysis of model behaviors
Qualitative exploration of stability and generalization
Introduced a unified model for understanding machine learning behaviors
Highlighted key factors affecting generalization performance
Demonstrated theoretical stability across various machine learning paradigms

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Implication

This investigation proposes a theoretical framework connecting stability, generalization, and solvability in machine learning, suggesting new insights into model performance.

Abstract

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Sandeep Kasukurthi (2026) studied this question.

synapsesocial.com/papers/698585ea8f7c464f23009a89 https://doi.org/10.5281/zenodo.18481240

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Unified Theory of Machine Learning : A Dynamical Theory of Stability, Generalization, and Solvability

Key Points

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Implication

Abstract

Discussion

Cite This Study

Unified Theory of Machine Learning : A Dynamical Theory of Stability, Generalization, and Solvability

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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