43 citations · 64 across the 6 of their papers we have counts for
5 papers · 1 filter
Transport Gaussian Processes for Regression
Gonzalo Rios
Gaussian process (GP) priors are non-parametric generative models with appealing modelling properties for Bayesian inference: they can model non-linear relationships through noisy…
Compositionally-Warped Gaussian Processes
Gonzalo Rios, Felipe Tobar
The Gaussian process (GP) is a nonparametric prior distribution over functions indexed by time, space, or other high-dimensional index set. The GP is a flexible model yet its limit…
Bayesian Learning with Wasserstein Barycenters
Julio Backhoff-Veraguas, Joaquin Fontbona, Gonzalo Rios +1
We introduce and study a novel model-selection strategy for Bayesian learning, based on optimal transport, along with its associated predictive posterior law: the Wasserstein popul…
Learning non-Gaussian Time Series using the Box-Cox Gaussian Process
Gonzalo Rios, Felipe Tobar
Gaussian processes (GPs) are Bayesian nonparametric generative models that provide interpretability of hyperparameters, admit closed-form expressions for training and inference, an…
Recovering Latent Signals from a Mixture of Measurements using a Gaussian Process Prior
Felipe Tobar, Gonzalo Rios, Tomás Valdivia +1
In sensing applications, sensors cannot always measure the latent quantity of interest at the required resolution, sometimes they can only acquire a blurred version of it due the s…