Active Level Set Estimation for Continuous Search Space with Theoretical Guarantee
This addresses the challenge of efficiently finding level sets in continuous domains for applications like optimization and modeling, offering a solution with proven convergence, though it is incremental in improving existing methods.
The paper tackles the problem of level set estimation for black-box functions in continuous search spaces, proposing a novel algorithm that avoids discretization and provides theoretical convergence guarantees, outperforming state-of-the-art methods on synthetic and real-world datasets.
A common problem encountered in many real-world applications is level set estimation where the goal is to determine the region in the function domain where the function is above or below a given threshold. When the function is black-box and expensive to evaluate, the level sets need to be found in a minimum set of function evaluations. Existing methods often assume a discrete search space with a finite set of data points for function evaluations and estimating the level sets. When applied to a continuous search space, these methods often need to first discretize the space which leads to poor results while needing high computational time. While some methods cater for the continuous setting, they still lack a proper guarantee for theoretical convergence. To address this problem, we propose a novel algorithm that does not need any discretization and can directly work in continuous search spaces. Our method suggests points by constructing an acquisition function that is defined as a measure of confidence of the function being higher or lower than the given threshold. A theoretical analysis for the convergence of the algorithm to an accurate solution is provided. On multiple synthetic and real-world datasets, our algorithm successfully outperforms state-of-the-art methods.