BO-pro-c-tr reduces regret and overhead in large-scale Bayesian optimization
Achieves 2.6% reduction in simple regret and 34.9% in computational overhead compared to independent local GP models.
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Achieves 2.6% reduction in simple regret and 34.9% in computational overhead compared to independent local GP models.
One-step EI stopping balances expected improvement against evaluation costs in freeze-thaw Bayesian optimization.
The proposed acquisition is globally solvable faster than multi-start quasi-Newton and matches Gaussian-process accuracy per evaluation on cheaper black boxes.
Precomputed approximations enable information-directed sampling and are shown on two synthetic-biology simulators with intractable likelihoods.
The methods target continuous and combinatorial inputs from about 10 to more than 6000 dimensions by using linear structure already in BO.
AIM manages research ideas with Bayesian-style selection, beating AutoLab baselines by up to 4.9 points and converging 3.1x faster.
A Gaussian-process preference model shares nearby-context feedback to personalize ankle and elbow exoskeleton assistance with less data.
JAREX maps multi-objective pharmaceutical process pass regions with fewer experiments than factorial design.
Shared latent traffic models calibrate urban simulators and cut travel time by up to 51% with deep Q-learning.
RS-MFBO couples global sensitivity analysis with fidelity-augmented Gaussian processes to slash costly high-fidelity simulation runs in industrial flowsheet optimization.
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