High Dimensional Bayesian Optimization

High-dimensional Bayesian optimization (HDBO) tackles the challenge of efficiently finding optimal solutions for complex problems with numerous input variables, where evaluating each solution is computationally expensive. Current research focuses on developing novel algorithms that overcome the limitations of standard Bayesian optimization in high dimensions, including methods based on random projections, coordinate-wise optimization, covariance matrix adaptation, and the integration of deep learning models like recurrent neural networks and transformers with Gaussian processes. These advancements aim to improve the scalability and efficiency of HDBO, enabling its application to diverse fields such as drug discovery, materials science, and robotics, where high-dimensional optimization problems are prevalent. The ultimate goal is to enhance the sample efficiency and reliability of optimization methods for complex, high-dimensional systems.