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Related Questions
- What is the purpose of the acquisition function in Bayesian optimization, and how does it relate to the convergence rate?
- Can you explain the different types of acquisition functions used in Bayesian optimization, and their respective impacts on convergence rate?
- How does the acquisition function balance exploration and exploitation in Bayesian optimization, and what effect does this have on convergence rate?
- In what scenarios does the acquisition function have a significant impact on the convergence rate of Bayesian optimization, and when is it less important?
- Are there any strategies for adapting the acquisition function to improve convergence rate in Bayesian optimization, especially in high-dimensional search spaces?
- Can you compare the convergence rates of Bayesian optimization with different acquisition functions, such as expected improvement and probability of improvement?
- How does the choice of acquisition function interact with other components of Bayesian optimization, such as the surrogate model and the search space, to affect convergence rate?
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