Kernel Change-point Detection with Auxiliary Deep Generative Models

Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabás Póczos

Abstract: 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 as a surrogate to the abnormal distribution.