Paper accepted for poster presentation at NeurIPS2021
Authors Pablo Moreno-Muñoz, Antonio Artes-Rodríguez, Mauricio Álvarez Abstract We present a framework for transfer learning based on modular variational Gaussian processes (GP). We develop a module-based method that having a dictionary of well fitted GPs, each model being characterised by its hyperparameters, pseudo-inputs and their corresponding posterior densities, one could build ensemble GP models without…