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Antonio Artés

2 accepted papers

2021

Modular Gaussian Processes for Transfer Learning

NeurIPS 2021poster

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…

2018

Heterogeneous Multi-output Gaussian Process Prediction

NeurIPS 2018spotlight

We present a novel extension of multi-output Gaussian processes for handling heterogeneous outputs. We assume that each output has its own likelihood function and use a vector-valued Gaussian process prior to jointly model the parameters in all likelihoods as latent functions. Our multi-output Gauss…