Gaussian Process Optimisation with Multi-fidelity Evaluations
Gaussian Process Optimisation with Multi-fidelity Evaluations
复制标题
具有多保真度评估的高斯过程优化
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
B. Póczos
中科院分区：
文献类型：
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作者：
Kirthevasan Kandasamy;Gautam Dasarathy;Junier B. Oliva;J. Schneider;B. Póczos
In many scientific and engineering applications, we are tasked with the optimisation of an expensive to evaluate black box function f . Traditional methods for this problem assume just the availability of this single function. However, in many cases, cheap approximations to f may be obtainable. For example, the expensive real world behaviour of a robot can be approximated by a cheap computer simulation. We can use these approximations to eliminate low function value regions cheaply and use the expensive evaluations of f in a small but promising region and speedily identify the optimum. We formalise this task as a multi-fidelity bandit problem where the target function and its approximations are sampled from a Gaussian process. We develop MF-GP-UCB, a novel method based on upper confidence bound techniques. In our theoretical analysis we demonstrate that it exhibits precisely the above behaviour, and achieves better regret than strategies which ignore multi-fidelity information. MF-GP-UCB outperforms such naive strategies and other multi-fidelity methods on several synthetic and real experiments.
