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This talk introduces Bayesian Optimization (BO), a sample-efficient framework for optimizing expensive, noisy black-box functions. It will cover the core ideas behind surrogate modeling (e.g., Gaussian processes) and acquisition functions (such as EI and UCB) that balance exploration and exploitation to find high-performing solutions with ...
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