Active Learning for Abrupt Shifts Change-point Detection via
  Derivative-Aware Gaussian Processes
Hao Zhao Rong Pan
Change-point detection (CPD) is crucial for identifying abrupt shifts in
data, which influence decision-making and efficient resource allocation across
various domains. To address the challenges posed by the costly and
time-intensive data acquisition in CPD, we introduce the Derivative-Aware
Change Detection (DACD) method. It leverages the derivative process of a
Gaussian process (GP) for Active Learning (AL), aiming to pinpoint change-point
locations effectively. DACD balances the exploitation and exploration of
derivative processes through multiple data acquisition functions (AFs). By
utilizing GP derivative mean and variance as criteria, DACD sequentially
selects the next sampling data point, thus enhancing algorithmic efficiency and
ensuring reliable and accurate results. We investigate the effectiveness of
DACD method in diverse scenarios and show it outperforms other active learning
change-point detection approaches.