Abstract: Preferential Bayesian optimization (PBO) is a framework for human-in-the-loop optimization to maximize black-box human preference functions such as seeking perceptually good visual designs.
Abstract: This work contributes with a new approach for tuning hyperparameters of machine learning models, based on sequences of optimization studies based on an initial range of hyperparameters.
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