43 citations · 64 across the 6 of their papers we have counts for
7 papers
Stochastic Gradient Descent for Barycenters in Wasserstein Space
Julio Backhoff-Veraguas, Joaquin Fontbona, Gonzalo Rios +1
We present and study a novel algorithm for the computation of 2-Wasserstein population barycenters of absolutely continuous probability measures on Euclidean space. The proposed me…
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…
A simple scheme for the parallelization of particle filters and its application to the tracking of complex stochastic systems
Dan Crisan, Joaquin Miguez, Gonzalo Rios
We investigate the use of possibly the simplest scheme for the parallelisation of the standard particle filter, that consists in splitting the computational budget into fully i…