Multi-Fidelity Bayesian Optimization With Across-Task Transferable Max-Value Entropy Search

arXiv:2403.09570v1 Announce Type: new
Abstract: In many applications, ranging from logistics to engineering, a designer is faced with a sequence of optimization tasks for which the objectives are in the form of black-box functions that are costly to evaluate. For example, the designer may need to tune the hyperparameters of neural network models for different learning tasks over time. Rather than evaluating the objective function for each candidate solution, the designer may have access to approximations of the objective functions, …

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