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- What are some common techniques used for hyperparameter tuning in Bayesian optimization, especially for expensive objective functions and limited iteration counts?
- How does Bayesian optimization adapt to the available budget and iteration count to optimize hyperparameters for expensive objective functions?
- What is the difference between Bayesian optimization and other hyperparameter tuning techniques, such as grid search or random search, in the context of expensive objective functions and limited iteration counts?
- Can you elaborate on the role of probabilistic models in Bayesian optimization, particularly for modeling the posterior distribution of hyperparameters?
- How does Bayesian optimization handle the trade-off between exploration and exploitation when dealing with expensive objective functions and limited iteration counts?
- What are some common challenges in implementing Bayesian optimization for expensive objective functions and limited iteration counts, and how can they be addressed?
- Can you discuss the relationship between the choice of acquisition function and the performance of Bayesian optimization for expensive objective functions and limited iteration counts?
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