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An architecture search framework that makes the alignment between experts and the structure of the data an explicit search variable and ensures that the assignment of data clusters to experts is optimised jointly with the per-expert architectures is proposed.
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 bo...
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