3 accepted papers
Bayesian Optimization (BO) is a popular black-box optimization method consisting of a surrogate model, typically a probabilistic model such as a Gaussian Process (GP) and an Acquisition function (AF). Effective selection of these functions has a strong impact on the optimization process. Existing en…
This paper introduces a novel structure for deep Gaussian processes (DGPs) and a method for determining their depths. The proposed framework enables faster convergence of their parameters and reduces computational cost to optimize them while maintaining performance comparable to that of conventional…
The inherent adaptability and flexibility of Gaussian processes lie in the capability of their kernel functions to capture diverse data characteristics. Thus, selecting an appropriate kernel function is crucial because an improper choice can detrimentally affect the model’s performance. One way to e…