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Related Questions
- What techniques does Bayesian optimization use to handle noisy objective functions?
- How does Bayesian optimization adjust its acquisition function to minimize the risk of noisy function evaluations?
- Can Bayesian optimization be used to handle expensive objective functions, such as simulations or experiments with long run times?
- What is the impact of using a smaller budget on Bayesian optimization for expensive objective functions?
- How does the choice of probability distribution, such as a Gaussian Process or a random forest, affect the performance of Bayesian optimization on noisy or expensive objective functions?
- What is the effect of incorporating domain knowledge or prior information into the Bayesian optimization process?
- Can Bayesian optimization be used to handle multi-fidelity objective functions, where different evaluations have different costs or complexities?
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