Bayesian optimization (BO) conventionally relies on handcrafted acquisition functions (AFs) to sequentially determine the sample points. However, it has been widely observed in practice that the best-performing AF in terms of regret can vary significantly under different types of black-box functions. It has remained a challenge to design one AF that can attain the best performance over a wide variety of black-box functions.

We initiate the study of behavioral information design through deep learning.

Meta-learning has shown its effectiveness in adapting to new tasks with transferring the prior experience learned from other related tasks [7,34,38]. At ahigh level, the meta-learning process involves two steps: meta-training and meta-testing.

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