  Detecting the emergence of abrupt property changes in time series is a
challenging problem. Kernel two-sample test has been studied for this task
which makes fewer assumptions on the distributions than traditional parametric
approaches. However, selecting kernels is non-trivial in practice. Although
kernel selection for two-sample test has been studied, the insufficient samples
in change point detection problem hinder the success of those developed kernel
selection algorithms. In this paper, we propose KL-CPD, a novel kernel learning
framework for time series CPD that optimizes a lower bound of test power via an
auxiliary generative model. With deep kernel parameterization, KL-CPD endows
kernel two-sample test with the data-driven kernel to detect different types of
change-points in real-world applications. The proposed approach significantly
outperformed other state-of-the-art methods in our comparative evaluation of
benchmark datasets and simulation studies.
