Machine learning models can be improved by adapting them to respect existing background knowledge. In this paper we consider multitask Gaussian processes, with background knowledge in the form of constraints that require a specific sum of the outputs to be constant. This is achieved by conditioning the prior distribution on the constraint fulfillment. The approach allows for both linear and nonlinear constraints. We demonstrate that the constraints are fulfilled with high precision and that the construction can improve the overall prediction accuracy as compared to the standard Gaussian process.

翻译：机器学习模型可通过适应现有背景知识来改进。本文考虑多任务高斯过程，其背景知识以约束形式呈现，要求输出的特定求和为常数。这一目标通过对先验分布施加约束满足条件来实现。该方法同时支持线性与非线性约束。我们证明约束能以高精度满足，且相较于标准高斯过程，该构建能提升整体预测精度。