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Bayesian optimization (BO) is a popular approach to hyperparameter tuning in machine learning. Today, we're breaking down how it works and the intuitions behind it. As well, we will briefly discussing the applications of BO to further black-box optimization problems, including those in active McGill AI Lab projects! The explanations are by ...
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Bayesian optimization is a method used to optimize complex functions that are expensive or time-consuming to evaluate. It is widely applied in machine learning, engineering, and scientific research to improve efficiency and precision, particularly in scenarios like hyperparameter tuning and experimental design. ✔️ Efficient Function Evaluation: Bayesian optimization minimizes costly evaluations, making it ideal for scenarios where each evaluation has a high resource cost. ✔️ Versatility: It hand
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