@inproceedings{
kokubun2023local,
title={Local Acquisition Function for Active Level Set Estimation},
author={Yuta Kokubun and Kota Matsui and Kentaro Kutsukake and Wataru Kumagai and Takafumi Kanamori},
booktitle={NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World},
year={2023},
url={https://openreview.net/forum?id=2aOKjoPwT4}
}
In this paper, we propose a new acquisition function based on local search for active super-level set estimation. Conventional acquisition functions for level set estimation problems are considered to struggle with problems where the threshold is high, and many points in the upper-level set have function values close to the threshold. The proposed method addresses this issue by effectively switching between two acquisition functions: one rapidly finds local level set and the other performs global exploration. The effectiveness of the proposed method is evaluated through experiments with synthetic and real-world datasets.
Local Acquisition Function for Active Level Set Estimation
Yuta Kokubun Kota Matsui Kentaro Kutsukake Wataru Kumagai Takafumi Kanamori