Bayesian Optimization for Adaptive Experimental Design: A Review
By Stewart Greenhill, Santu Rana, Sunil Gupta, Pratibha Vellanki, Svetha Venkatesh. IEEE Access, 2020
398 citations recorded by the source corpus.
Bayesian optimisation is a statistical method that efficiently models and optimises expensive “black-box” functions. This review considers the application of Bayesian optimisation to experimental design, in comparison to existing Design of Experiments (DOE) methods. Solutions are surveyed for a range of core issues in experimental design including: the incorporation of prior knowledge, high dimensional optimisation, constraints, batch evaluation, multiple objectives, multi-fidelity data, and mixed variable types.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/9c2feda6ec5df0161e2cbeac5e46e6f0ce735424
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Citations in the corpus
- AutoScale: Energy Efficiency Optimization for Stochastic Edge Inference Using Reinforcement Learning (2020, methodology): “…the inefficiency of existing prediction-based approaches, we compare three types of prediction-based approaches with the baseline (Edge (CPU)) and oracular design (Opt): 1) regression-based approaches, 2) classification-based approaches, and 3) Bayesian optimization-based approach [32], [39], [92].”