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Nagoya Institute of Technology
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A single information-theoretic framework elegantly unifies multi-objective, multi-fidelity, and constrained Bayesian optimization, bypassing separate heuristics for each.
Optimizing BO hyperparameters with BO itself reveals configurations that significantly reduce cumulative regret on GP-inferred sample paths.
