statistical-learning-theory

Singular Learning Theory (SLT) uses algebraic geometry to explain why neural networks generalize well despite their degeneracies, introducing the real log canonical threshold (RLCT) as a measure of model complexity.

This paper introduces a theoretical framework for quantifying deployment risk when training and deployment distributions differ due to latent regime dynamics modeled as a Markov-switching process, providing exact decomposition and finite-sample bounds.

FormalSLT is a Lean 4 library that formally proves finite-sample statistical learning theory results (ERM, VC bounds, Rademacher bounds, PAC-Bayes, etc.) with explicit assumptions and zero sorry statements, providing a machine-checked foundation for ML theory.