What are the main challenges in Bayesian optimization?

What are the main challenges in Bayesian optimization?

Some key challenges include selecting the right kernel, handling noisy observations, and managing computational costs.

Can you explain why selecting the right kernel is important?

The kernel defines the similarity between points; a poor choice can lead to inaccurate predictions and suboptimal performance.

What strategies can be used to handle noisy observations?

Techniques like robust optimization, using noise models, or incorporating prior knowledge can help mitigate noise effects.

How does Bayesian optimization compare to other optimization methods?

Bayesian optimization is particularly effective for expensive function evaluations, unlike gradient-based methods that require derivatives.