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Related Questions
- How do entropy-based acquisition functions balance exploration and exploitation for multi-modal objective functions?
- Can you elaborate on the differences between Expected Improvement (EI) and Probability of Improvement (PI) in handling multi-modality?
- In what scenarios do entropy-based acquisition functions tend to work better than other methods such as Thompson sampling or progress-based acquisition functions?
- How do entropy-based acquisition functions handle non-smooth or noisy objective functions in multi-modal optimization problems?
- Are there any specific hyperparameters in entropy-based acquisition functions that need to be tuned to handle multi-modal objective functions effectively?
- Can you provide an example use case where entropy-based acquisition functions outperformed other acquisition functions in a real-world multi-modal optimization problem?
- How do entropy-based acquisition functions compare to other methods such as Gaussian processes or random forests in handling multi-modal objective functions?
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