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Related Questions
- How do acquisition functions like EI (Expected Improvement) or PI (Probability of Improvement) adapt to noisy evaluations in Bayesian optimization?
- What are some strategies to mitigate the impact of noisy evaluations on the acquisition function's performance?
- How do changes in problem constraints affect the acquisition function's behavior, and what techniques can be used to handle such changes?
- Can you explain how to balance exploration and exploitation in the acquisition function when faced with sudden changes in the objective function?
- What are some common pitfalls to avoid when using acquisition functions in scenarios with rapidly changing problem constraints?
- How can you modify the acquisition function to incorporate feedback from previous evaluations and adapt to changes in the problem's constraints?
- What are some acquisition function variants that are more robust to sudden changes in the objective function due to noisy evaluations or changes in problem constraints?
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