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Related Questions
- What are the key differences between Bayesian optimization using probabilistic models and traditional optimization methods?
- How do probabilistic models in Bayesian optimization handle non-stationarity in the objective function over time?
- Can you explain the concept of 'elites' in Bayesian optimization and how it relates to adapting to changes in the objective function?
- What are some common techniques used in Bayesian optimization to adapt to changes in the objective function, such as re-learning or re-starting the optimization process?
- How do probabilistic models in Bayesian optimization handle exploration-exploitation trade-offs in the presence of changing objective functions?
- What is the role of surrogate models in Bayesian optimization, and how do they contribute to adapting to changes in the objective function?
- Can you discuss the impact of time-varying objective functions on the performance of Bayesian optimization algorithms, and how to mitigate its effects?
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