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Related Questions
- What are some common sources of noise or uncertainty in real-world data and how can domain knowledge help mitigate their impact on Bayesian optimization?
- How can domain knowledge be used to inform the choice of acquisition function in Bayesian optimization, particularly in cases where data is noisy or uncertain?
- What role can prior knowledge from domain experts play in defining the search space and objective function for Bayesian optimization, and how can this improve robustness to noisy data?
- Can domain knowledge be used to develop custom surrogates or approximations of the objective function that are more robust to noise and uncertainty, and if so, how?
- In what ways can domain knowledge be used to incorporate additional constraints or regularization terms into the Bayesian optimization process to improve robustness to noisy data?
- How can domain knowledge be used to select the most informative data points to collect or prioritize in Bayesian optimization, particularly in cases where data is limited or noisy?
- What are some strategies for using domain knowledge to adapt Bayesian optimization to changing or non-stationary environments, where data may be noisy or uncertain?
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