- Session 4: Unsupervised, Semi-supervised Learning, Reinforcement Learning -- Day 3 (Nov.19), talks: 10:50-11:30 (5th floor Hall 2), poster session: 11:30-14:00
- Poster number: Tue21
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Shogo Hayashi (Kyoto University); Yoshinobu Kawahara (Kyushu University / RIKEN); Hisashi Kashima (Kyoto University/RIKEN Center for AIP)
We introduce Active Change-Point Detection (ACPD), a novel active learning problem for efficient change-point detection in situations where the cost of data acquisition is expensive. At each round of ACPD, the task is to adaptively determine the next input, in order to detect the change-point in a black-box expensive-to-evaluate function, with as few evaluations as possible. We propose a novel framework that can be generalized for different types of data and change-points, by utilizing an existing change-point detection method to compute change scores and the Bayesian optimization method to determine the next input. We demonstrate the efficiency of our proposed framework in different settings of datasets and change-points, using synthetic data and real-world data, such as material science data and seafloor depth data.