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Related Questions
- What are the key differences between Bayesian optimization and other black-box optimization techniques?
- How do surrogate models handle noisy objective functions in Bayesian optimization, and what are the benefits of this approach?
- Can you explain the concept of acquisition functions in Bayesian optimization and how they relate to surrogate models?
- How do surrogate models update their predictions when new data points are added, and what is the impact on the optimization process?
- In what scenarios do surrogate models perform particularly well in handling noisy objective functions, and what are the limitations of this approach?
- Can you compare and contrast the use of Gaussian processes and random forests as surrogate models in Bayesian optimization?
- How do surrogate models handle multi-modal objective functions, and what strategies can be employed to improve the convergence of Bayesian optimization in such cases?
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