An Adaptive Dropout Approach for High-Dimensional Bayesian Optimization
This provides an efficient solution for high-dimensional expensive optimization problems, though it appears incremental as it builds on existing Bayesian optimization frameworks.
The paper tackles the problem of Bayesian optimization's performance degradation in high-dimensional settings by adaptively dropping variables from the acquisition function, resulting in improved solution quality and superior results compared to state-of-the-art methods.
Bayesian optimization (BO) is a widely used algorithm for solving expensive black-box optimization problems. However, its performance decreases significantly on high-dimensional problems due to the inherent high-dimensionality of the acquisition function. In the proposed algorithm, we adaptively dropout the variables of the acquisition function along the iterations. By gradually reducing the dimension of the acquisition function, the proposed approach has less and less difficulty to optimize the acquisition function. Numerical experiments demonstrate that AdaDropout effectively tackle high-dimensional challenges and improve solution quality where standard Bayesian optimization methods often struggle. Moreover, it achieves superior results when compared with state-of-the-art high-dimensional Bayesian optimization approaches. This work provides a simple yet efficient solution for high-dimensional expensive optimization.