7 papers
Causality as the Statistical Conscience of Artificial Intelligence: From Pearl's Ladder to Trustworthy Machines
Ernest Fokoué
Modern Artificial Intelligence achieves remarkable predictive power by optimizing statistical risk functionals over vast corpora. Yet a gap separates this from genuine intelligence…
Isomorphic Functionalities between Ant Colony and Ensemble Learning: Part III -- Gradient Descent, Neural Plasticity, and the Emergence of Deep Intelligence
Ernest Fokoué, Gregory Babbitt, Yuval Levental
In Parts I and II of this series, we established isomorphisms between ant colony decision-making and two major families of ensemble learning: random forests (parallel, variance red…
Isomorphic Functionalities between Ant Colony and Ensemble Learning: Part II-On the Strength of Weak Learnability and the Boosting Paradigm
Ernest Fokoué, Gregory Babbitt, Yuval Levental
In Part I of this series, we established a rigorous mathematical isomorphism between ant colony decision-making and random forest learning, demonstrating that variance reduction th…
Decorrelation, Diversity, and Emergent Intelligence: The Isomorphism Between Social Insect Colonies and Ensemble Machine Learning
Ernest Fokoué, Gregory Babbitt, Yuval Levental
Social insect colonies and ensemble machine learning methods represent two of the most successful examples of decentralized information processing in nature and computation respect…
Fibonacci-Driven Recursive Ensembles: Algorithms, Convergence, and Learning Dynamics
Ernest Fokoué
This paper develops the algorithmic and dynamical foundations of recursive ensemble learning driven by Fibonacci-type update flows. In contrast with classical boosting Freund and S…
A General Weighting Theory for Ensemble Learning: Beyond Variance Reduction via Spectral and Geometric Structure
Ernest Fokoué
Ensemble learning is traditionally justified as a variance-reduction strategy, explaining its strong performance for unstable predictors such as decision trees. This explanation, h…