[2M5-OS-24] OS-24

[2M5-OS-24-01] A stopping criterion for level set estimation

〇Hideaki Ishibashi¹, Kota Matsui², Kentaro Kutsukake³, Hideitsu Hino^4,3 (1. Kyushu Institute of Technology, 2. Nagoya University, 3. RIKEN Center for Advanced Intelligence Project, 4. The Institute of Statistical Mathematics)

Keywords:Level set estimation, Adaptive experimental design, Stopping criterion, Gaussian process

Level set estimation is one of the adaptive experimental design that determines the next measurement point by using the obtained measurement results so far, and its task is to estimate the regions that do not satisfy the desired level using as few data as possible. Level set estimation considers a black box function with each measurement point as an input and the corresponding measurement result as an output, and predicts whether unmeasurement point exceeds the threshold using a surrogate function estimated from the dataset. The efficiency of level set estimation depends on (1) the acquisition function that determines the next measurement point and (2) the timing at which level set estimation is stopped. This study proposes a stopping criterion for level set estimation based on the probability that the surrogate function exceeds the threshold value. The proposed stopping criterion can guarantee a tail probability that the surrogate function exceeds the threshold for any acquisition function. This paper shows that the proposed stopping criterion can efficiently stop level set estimation for several test functions.