Gaussian Processes and the Kullback-Leibler divergence have been deeply studied in Statistics and Machine Learning. This paper marries these two concepts and introduce the local Kullback-Leibler divergence to learn about intervals where two Gaussian Processes differ the most. We address subtleties entailed in the estimation of local divergences and the corresponding interval of local maximum divergence as well. The estimation performance and the numerical efficiency of the proposed method are showcased via a Monte Carlo simulation study. In a medical research context, we assess the potential of the devised tools in the analysis of electrocardiogram signals.

翻译：高斯过程与Kullback-Leibler散度在统计学和机器学习领域已被深入研究。本文融合这两个概念，引入局部Kullback-Leibler散度，用于学习两个高斯过程差异最大的区间。我们还探讨了局部散度估计及相应局部最大散度区间估计所涉及的微妙问题。通过蒙特卡洛模拟研究，展示了所提方法的估计性能与数值效率。在医学研究背景下，我们评估了所开发工具在心电信号分析中的潜力。