The paper "Taking the Human Out of the Loop: A Review of Bayesian Optimization" by Shahriari et al. (2016) provides a comprehensive review of Bayesian optimization, a powerful tool for optimizing design choices in large-scale systems. The authors highlight the importance of automating design choices to improve product quality and human productivity. Bayesian optimization is particularly useful in scenarios where evaluations of the objective function are costly, derivatives are not available, and the function is non-convex and multimodal. The paper introduces the key components of Bayesian optimization, including a probabilistic surrogate model and a loss function that describes the optimal sequence of queries. The surrogate model captures beliefs about the unknown objective function, while the loss function evaluates the utility of candidate points. The authors discuss various statistical models, such as parametric and non-parametric models, and acquisition functions, which guide the exploration and exploitation trade-offs. The paper also covers applications of Bayesian optimization in areas like A/B testing, recommender systems, robotics, environmental monitoring, preference learning, automatic machine learning, combinatorial optimization, and natural language processing. It provides detailed examples and illustrations to explain the concepts and techniques used in Bayesian optimization. Overall, the paper aims to disentangle the multiple components that determine the success of Bayesian optimization implementations, emphasizing the importance of statistical modeling and the careful choice of statistical models over acquisition function heuristics.The paper "Taking the Human Out of the Loop: A Review of Bayesian Optimization" by Shahriari et al. (2016) provides a comprehensive review of Bayesian optimization, a powerful tool for optimizing design choices in large-scale systems. The authors highlight the importance of automating design choices to improve product quality and human productivity. Bayesian optimization is particularly useful in scenarios where evaluations of the objective function are costly, derivatives are not available, and the function is non-convex and multimodal. The paper introduces the key components of Bayesian optimization, including a probabilistic surrogate model and a loss function that describes the optimal sequence of queries. The surrogate model captures beliefs about the unknown objective function, while the loss function evaluates the utility of candidate points. The authors discuss various statistical models, such as parametric and non-parametric models, and acquisition functions, which guide the exploration and exploitation trade-offs. The paper also covers applications of Bayesian optimization in areas like A/B testing, recommender systems, robotics, environmental monitoring, preference learning, automatic machine learning, combinatorial optimization, and natural language processing. It provides detailed examples and illustrations to explain the concepts and techniques used in Bayesian optimization. Overall, the paper aims to disentangle the multiple components that determine the success of Bayesian optimization implementations, emphasizing the importance of statistical modeling and the careful choice of statistical models over acquisition function heuristics.