论文标题
通过增强的高斯流程和多信息源优化的绿色机器学习
Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization
论文作者
论文摘要
搜索准确的机器和深度学习模型是一个计算上昂贵且极具能量的过程。最近对大幅度减少计算时间和消耗的能源的策略最近变得重要，以利用不同信息源的可用性，具有不同的计算成本和不同的“保真度”，通常是大型数据集中较小的部分。多源优化策略符合基于高斯过程的贝叶斯优化的方案。提出了一种增强的高斯工艺方法，该方法利用了多个信息源（即AGP-MISO）。增强的高斯流程仅在可用来源之间仅使用“可靠”信息培训。根据增强的高斯过程定义了一种新颖的采集函数。报告了计算结果与使用两个资源的支持向量机（SVM）分类器的超参数的优化有关：一个大数据集（最昂贵的数据集）以及其中的较小部分。仅报道了仅报告大型数据集中SVM分类器的超参数的传统贝叶斯优化方法的比较。
Searching for accurate Machine and Deep Learning models is a computationally expensive and awfully energivorous process. A strategy which has been gaining recently importance to drastically reduce computational time and energy consumed is to exploit the availability of different information sources, with different computational costs and different "fidelity", typically smaller portions of a large dataset. The multi-source optimization strategy fits into the scheme of Gaussian Process based Bayesian Optimization. An Augmented Gaussian Process method exploiting multiple information sources (namely, AGP-MISO) is proposed. The Augmented Gaussian Process is trained using only "reliable" information among available sources. A novel acquisition function is defined according to the Augmented Gaussian Process. Computational results are reported related to the optimization of the hyperparameters of a Support Vector Machine (SVM) classifier using two sources: a large dataset - the most expensive one - and a smaller portion of it. A comparison with a traditional Bayesian Optimization approach to optimize the hyperparameters of the SVM classifier on the large dataset only is reported.
