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

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