Deep Gaussian Processes for Multi-fidelity Modeling
Deep Gaussian Processes for Multi-fidelity Modeling
复制标题
用于多保真度建模的深度高斯过程
DOI：
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发表时间：
2019
期刊：
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通讯作者：
Javier I. González
中科院分区：
文献类型：
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作者：
Kurt Cutajar;Mark Pullin;Andreas C. Damianou;Neil D. Lawrence;Javier I. González
多保真度方法主要用于廉价获取，但可能存在偏差和噪声的情况，为了构建可靠的模型，必须将观测结果与有限或昂贵的真实数据有效地结合起来。这既出现在基本的机器学习过程中，如贝叶斯优化，也出现在更实际的科学和工程应用中。本文提出了一种新的多保真度模型，该模型将深度高斯过程的各层作为保真度级别，并使用变分推理方案在各层之间传播不确定性。这允许捕获保真度之间的非线性相关性，与利用成分结构的现有方法相比，过度拟合的风险更低，而成分结构反过来又受到结构假设和约束的负担。我们表明，所提出的方法在量化和传播多保真度设置中的不确定性方面取得了实质性的改进，从而提高了它们在决策管道中的有效性。
Multi-fidelity methods are prominently used when cheaply-obtained, but possibly biased and noisy, observations must be effectively combined with limited or expensive true data in order to construct reliable models. This arises in both fundamental machine learning procedures such as Bayesian optimization, as well as more practical science and engineering applications. In this paper we develop a novel multi-fidelity model which treats layers of a deep Gaussian process as fidelity levels, and uses a variational inference scheme to propagate uncertainty across them. This allows for capturing nonlinear correlations between fidelities with lower risk of overfitting than existing methods exploiting compositional structure, which are conversely burdened by structural assumptions and constraints. We show that the proposed approach makes substantial improvements in quantifying and propagating uncertainty in multi-fidelity set-ups, which in turn improves their effectiveness in decision making pipelines.