AI RESEARCH
Provably Reduced Sample Cost in Prior-Guided Hyperparameter Optimization
arXiv CS.LG
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ArXi:2606.04866v1 Announce Type: new Large-scale hyperparameter optimization (HPO) in automated machine learning (AutoML) consumes substantial computational resources, raising growing concerns about scalability and energy efficiency. Existing methods use prior information heuristically to accelerate both black-box and multi-fidelity settings, but they lack a characterization of how prior informativeness quantitatively reduces sample complexity.