Exact gaussian process regression with distributed computations
Duc-Trung Nguyen, Maurizio Filippone, Pietro Michiardi · 2019
Gaussian Processes (GPs) are powerful non-parametric Bayesian models for function estimation, but suffer from high complexity in terms of both computation and storage. To address such issues, approximation methods have flourished in the literature, including model approximations and approximate inference. However, these methods often sacrifice accuracy for scalability.