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Related Questions
- How does the choice of acquisition function in Bayesian optimization affect the trade-off between exploring the search space and exploiting known information?
- Can you explain the difference between acquisition functions like Upper Confidence Bound (UCB) and Probability of Improvement (PI) in terms of exploration-exploitation trade-off?
- How does the acquisition function balance the need to sample from new areas of the search space (exploration) versus sampling from promising regions (exploitation)?
- What role does the hyperparameter β (beta) play in UCB acquisition function, and how does it affect the exploration-exploitation trade-off?
- How does the acquisition function adapt to changes in the search space, such as a sudden improvement in objective function value?
- Can you compare and contrast the acquisition functions used in Bayesian optimization, such as EI (Expected Improvement) and GP-UCB, in terms of exploration-exploitation trade-off?
- How does the choice of acquisition function influence the convergence rate and optimization quality in Bayesian optimization?
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