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Graph-Based Bayesian Optimization for Large-Scale Objective-Based Experimental Design
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DOI：10.1109/TNNLS.2021.3071958.png)
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摘要
En 中文
Design is an inseparable part of most scientific and engineering tasks, including real and simulation-based experimental design processes and parameter/hyperparameter tuning/optimization. Several model-based experimental design techniques have been developed for design in domains with partial available knowledge about the underlying process. This article focuses on a powerful class of model-based experimental design called the mean objective cost of uncertainty (MOCU). The MOCU-based techniques are objective-based, meaning that they take the main objective of the process into account during the experimental design process. However, the lack of scalability of MOCU-based techniques prevents their application to most practical problems, including large discrete or combinatorial spaces. To achieve a scalable objective-based experimental design, this article proposes a graph-based MOCU-based Bayesian optimization framework. The correlations among samples in the large design space are accounted for using a graph-based Gaussian process, and an efficient closed-form sequential selection is achieved through the well-known expected improvement policy. The proposed framework's performance is assessed through the structural intervention in gene regulatory networks, aiming to make the network away from the states associated with cancer.
Keyword:
Uncertainty
Genomics
Bioinformatics
Bayes methods
Task analysis
Computational modeling
Tuning
Bayesian optimization
gene regulatory networks (GRNs)
graphical models
mean objective cost of uncertainty (MOCU)
model-based experimental design
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8.9
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7.5K
被引数:
7.2W
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