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Related Questions
- What are the advantages and disadvantages of using entropy-based acquisition functions in Bayesian optimization compared to probability-based acquisition functions?
- How do entropy-based acquisition functions handle uncertainty in the objective function, and what are the implications for the optimization process?
- Can you explain the concept of expected improvement and its role in probability-based acquisition functions, and how it differs from entropy-based acquisition functions?
- What are the key assumptions made by probability-based acquisition functions, and under what conditions do they break down in Bayesian optimization?
- How do entropy-based acquisition functions handle multi-modal objective functions, and what strategies can be used to improve robustness?
- What are the computational costs associated with entropy-based and probability-based acquisition functions, and how do they impact the overall optimization process?
- Can you compare and contrast the performance of entropy-based and probability-based acquisition functions on a variety of benchmark optimization problems?
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