论文标题
安全积极学习多输出高斯流程
Safe Active Learning for Multi-Output Gaussian Processes
论文作者
论文摘要
多输出回归问题通常在科学和工程中遇到。特别是，多输出高斯流程已成为建模这些复杂系统的有前途的工具，因为它们可以利用固有的相关性并提供可靠的不确定性估计。但是，在许多应用中，获取数据是昂贵的，并且可能会出现安全问题（例如机器人技术，工程）。我们为多输出高斯流程回归提出了一种安全的主动学习方法。此方法查询最有用的数据或输出，考虑了回归器之间的相关性和安全限制。我们通过提供理论分析并在模拟数据集和现实世界工程数据集中证明经验结果来证明我们的方法的有效性。在所有数据集中，与竞争对手相比，我们的方法都显示出改善的收敛性。
Multi-output regression problems are commonly encountered in science and engineering. In particular, multi-output Gaussian processes have been emerged as a promising tool for modeling these complex systems since they can exploit the inherent correlations and provide reliable uncertainty estimates. In many applications, however, acquiring the data is expensive and safety concerns might arise (e.g. robotics, engineering). We propose a safe active learning approach for multi-output Gaussian process regression. This approach queries the most informative data or output taking the relatedness between the regressors and safety constraints into account. We prove the effectiveness of our approach by providing theoretical analysis and by demonstrating empirical results on simulated datasets and on a real-world engineering dataset. On all datasets, our approach shows improved convergence compared to its competitors.
