Sparse Gaussian Process Regression

Sparse Gaussian Process Regression (SGPR) aims to overcome the computational limitations of standard Gaussian Processes by approximating the full model with a smaller, more manageable subset of data points. Current research focuses on improving the efficiency and scalability of SGPR through various techniques, including variational inference, inducing point methods, and distributed computing architectures. These advancements enable the application of GP regression to larger datasets and more complex problems, impacting fields like geomagnetic forecasting, robotics (e.g., ego-motion estimation), and multi-robot systems where efficient, uncertainty-aware modeling is crucial. The development of novel algorithms that balance accuracy and computational cost remains a central theme.