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Found 3 papers, 1 papers with code
Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning
Nonetheless, for hyperparameter tuning in deep neural networks, the time required to evaluate the validation error for even a few hyperparameter settings remains a bottleneck.
Bayesian Optimization with Expensive Integrands
We propose a Bayesian optimization algorithm for objective functions that are sums or integrals of expensive-to-evaluate functions, allowing noisy evaluations.
Stratified Bayesian Optimization
We consider derivative-free black-box global optimization of expensive noisy functions, when most of the randomness in the objective is produced by a few influential scalar random inputs.
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