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- W4372273129 abstract "Bayesian optimization (BO) is a powerful sequential optimization approach for seeking the global optimum of black-box functions for sample efficiency purposes. Evaluations of black-box functions can be expensive, rendering reduced use of labeled data desirable. For the first time, we introduce a teacher-student model, called $texttt{TSBO}$, to enable semi-supervised learning that can make use of large amounts of cheaply generated unlabeled data under the context of BO to enhance the generalization of data query models. Our teacher-student model is uncertainty-aware and offers a practical mechanism for leveraging the pseudo labels generated for unlabeled data while dealing with the involved risk. We show that the selection of unlabeled data is key to $texttt{TSBO}$. We optimize unlabeled data sampling by generating unlabeled data from a dynamically fitted extreme value distribution or a parameterized sampling distribution learned by minimizing the student feedback. $texttt{TSBO}$ is capable of operating in a learned latent space with reduced dimensionality, providing scalability to high-dimensional problems. $texttt{TSBO}$ demonstrates the significant sample efficiency in several global optimization tasks under tight labeled data budgets." @default.
- W4372273129 created "2023-05-07" @default.
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- W4372273129 date "2023-05-04" @default.
- W4372273129 modified "2023-10-05" @default.
- W4372273129 title "High-dimensional Bayesian Optimization via Semi-supervised Learning with Optimized Unlabeled Data Sampling" @default.
- W4372273129 doi "https://doi.org/10.48550/arxiv.2305.02614" @default.
- W4372273129 hasPublicationYear "2023" @default.
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