Sparse Gaussian Process

Sparse Gaussian Processes (SGPs) are computationally efficient approximations of standard Gaussian Processes, addressing the latter's limitations in handling large datasets. Current research focuses on developing improved algorithms for inducing point selection and model adaptation, particularly within online learning frameworks and for applications involving high-dimensional data or non-stationary environments. These advancements enable SGPs to be applied to diverse fields, including robotics (navigation, mapping, control), environmental monitoring, and machine learning (surrogate modeling, Bayesian optimization), where their ability to balance accuracy and computational efficiency is crucial. The resulting models offer improved scalability and robustness compared to their full-GP counterparts, leading to more practical and reliable solutions in various domains.