Mean-Field-Type Learning
Abstract
This chapter focuses on learning algorithms. Adaptive moment methods are shown to fail structurally. Mean-field-type learning is proposed as a remedy. Boltzmann–Gibbs dynamics concentrate on global minimizers. Algorithmic constructions are explicit. Sample-level tests validate theory. The learning process is interpreted as population-level optimization. Stability replaces heuristics. The chapter provides a principled alternative to classical optimization in large-scale machine intelligence.
Fields of Study & Subjects
Machine Learning and Algorithms
Neural Networks and Applications
Stochastic Gradient Optimization Techniques
Publication details
- Journal / source
- Chapman & Hall/CRC eBooks
- Pages
- 175–212
- Publication date
- 6 February 2026
- Language
- EN
- Document type
- Book chapter