Acquisition Function

Acquisition functions are crucial components of Bayesian optimization, guiding the selection of data points to efficiently optimize expensive-to-evaluate functions. Current research focuses on developing more sophisticated acquisition functions that address limitations of existing methods, such as myopia (short-sightedness) and challenges in high-dimensional spaces, often employing techniques like multi-fidelity approaches, reinforcement learning, and large language models to improve performance. These advancements enhance the efficiency and robustness of Bayesian optimization across diverse fields, including materials science, drug discovery, and machine learning, by reducing the number of costly function evaluations needed to find optimal solutions.