Ziyi Huang

Ensemble Diversity Optimization for Subjective Supervision

Ensemble Diversity Optimization (EDO), a prediction-space framework that jointly optimizes ensemble weights, effective cardinality, and calibration through a unified differentiable objective, provides an efficient, model-agnostic approach for modeling human subjectivity in supervised learning.