An $(ε,δ)$-accurate level set estimation with a stopping criterion
This addresses inefficiencies in sequential optimization for costly function evaluations, though it is incremental by adding a stopping criterion to existing methods.
The paper tackles the level set estimation problem by introducing an acquisition strategy with a stopping criterion to reduce unnecessary function evaluations, and proves that the method achieves ε-accuracy with a confidence level of 1-δ while showing comparable precision in experiments.
The level set estimation problem seeks to identify regions within a set of candidate points where an unknown and costly to evaluate function's value exceeds a specified threshold, providing an efficient alternative to exhaustive evaluations of function values. Traditional methods often use sequential optimization strategies to find $ε$-accurate solutions, which permit a margin around the threshold contour but frequently lack effective stopping criteria, leading to excessive exploration and inefficiencies. This paper introduces an acquisition strategy for level set estimation that incorporates a stopping criterion, ensuring the algorithm halts when further exploration is unlikely to yield improvements, thereby reducing unnecessary function evaluations. We theoretically prove that our method satisfies $ε$-accuracy with a confidence level of $1 - δ$, addressing a key gap in existing approaches. Furthermore, we show that this also leads to guarantees on the lower bounds of performance metrics such as F-score. Numerical experiments demonstrate that the proposed acquisition function achieves comparable precision to existing methods while confirming that the stopping criterion effectively terminates the algorithm once adequate exploration is completed.