Alexandr Udeneev

Surrogate assisted diversity estimation in neural ensemble search

A dual-objective surrogate-guided ensemble search is introduced: candidate architectures are represented as directed acyclic graphs, and two surrogate models are trained independently to estimate predictive accuracy and diversity potential and guide an NES framework that efficiently identifies architectures that are both individually strong and collectively diverse.