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BONSAI is introduced, a default-aware BO policy that prunes low-impact deviations from a default configuration while explicitly controlling the loss in acquisition value and theoretically bound the regret incurred by BONSAI.
This work introduces Power-Law Entropy Search (PLES), a computational cost-aware acquisition function built on multi-fidelity Bayesian optimization that efficiently estimates optimal hyperparameter scaling laws through adaptive experimentation using less than one-tenth of the computational budget required by convention...
Bayesian optimization routinely warm-starts a target experiment with data from related source tasks, and the multi-task Gaussian process is the textbook surrogate for the job, but it is found that it misestimates the cross-task correlation even in the simplest non-trivial case.
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