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Related Questions
- What are the key assumptions and limitations of using a Gaussian process as a surrogate model in Bayesian optimization?
- How does the computational cost of training a Gaussian process compare to other popular surrogate models like random forests or neural networks?
- Can you provide examples of real-world applications where Gaussian processes have been successfully used as surrogate models in Bayesian optimization?
- What are some common pitfalls to avoid when using Gaussian processes in Bayesian optimization, and how can they be addressed?
- How does the choice of kernel function for the Gaussian process affect its performance in Bayesian optimization?
- Can you compare the exploration-exploitation trade-off in Gaussian process-based Bayesian optimization with other methods like epsilon-greedy or Thompson sampling?
- Are there any scalability issues when using Gaussian processes in Bayesian optimization for large or high-dimensional search spaces?
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