Enhancing Bayesian Optimization and Active Learning Through Kernel Diversity
A unified framework, KENDO (Kernel ENsemble Disagreement-aware Operator), is proposed that integrates Ensemble Gaussian Processes (EGP) with disagreement-aware acquisition strategies and extends the approach to multi-objective optimization via random scalarization that preserves the single-optimizer conditioning structure.
Heng Zhang, Haotian Xiang, Qin Lu et al.
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