Formalization of ML generalization bounds achieved in Lean 4

Researchers have formalized generalization error bounds using Rademacher complexity in the Lean 4 proof assistant. This work builds upon measure-theoretic probability theory within the Mathlib library. The formalization includes a mechanically-checked pipeline from definitions to high-probability uniform deviation bounds via a proved McDiarmid inequality, with applications to linear predictors and Dudley-type entropy integral bounds. AI

IMPACT Provides a mechanically-verified foundation for understanding machine learning model generalization, potentially improving trust and reliability in theoretical guarantees.

RANK_REASON This is a formalization of a theoretical concept in machine learning published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• Mathlib
• Sho Sonoda

• paper
• safety

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →