where 𝜆 is an acquisition function parameter that controls the tradeoff between exploration and exploitation. Specifically, if 𝜆 is small, then 𝑔(𝛾; 𝜆) will be dominated by 𝜇 𝑠 , meaning that we favor the solutions that have higher model performance. If 𝜆 is large, then 𝑔(𝛾; 𝜆) will be dominated by 𝜎 𝑠 , meaning that we prefer to explore the domains where we are uncertain about the model performance. We determine the next set of hyperparameters by maximizing this acquisition function: