43 citations · 139 across the 20 of their papers we have counts for
26 papers
Greedy online change point detection
Jou-Hui Ho, Felipe Tobar
Standard online change point detection (CPD) methods tend to have large false discovery rates as their detections are sensitive to outliers. To overcome this drawback, we propose G…
Gaussian process deconvolution
Felipe Tobar, Arnaud Robert, Jorge F. Silva
Let us consider the deconvolution problem, that is, to recover a latent source from the observations of a convolution process $y = x\star…
Computationally-efficient initialisation of GPs: The generalised variogram method
Felipe Tobar, Elsa Cazelles, Taco de Wolff
We present a computationally-efficient strategy to initialise the hyperparameters of a Gaussian process (GP) avoiding the computation of the likelihood function. Our strategy can b…
Detecting the periodicity of highly irregularly sampled light-curves with Gaussian processes: the case of SDSSJ025214.67-002813.7
Stefano Covino, Felipe Tobar, Aldo Treves
Based on a 20-year-long multiband observation of its light-curve, it was conjectured that the quasar SDSSJ025214.67-002813.7 has a periodicity of ~4.4 years. These observations wer…
Nonstationary multi-output Gaussian processes via harmonizable spectral mixtures
Matías Altamirano, Felipe Tobar
Kernel design for Multi-output Gaussian Processes (MOGP) has received increased attention recently. In particular, the Multi-Output Spectral Mixture kernel (MOSM) arXiv:1709.01298…
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…
Studying the Interplay between Information Loss and Operation Loss in Representations for Classification
Jorge F. Silva, Felipe Tobar, Mario Vicuña +1
Information-theoretic measures have been widely adopted in the design of features for learning and decision problems. Inspired by this, we look at the relationship between i) a wea…
Bayesian autoregressive spectral estimation
Alejandro Cuevas, Sebastián López, Danilo Mandic +1
Autoregressive (AR) time series models are widely used in parametric spectral estimation (SE), where the power spectral density (PSD) of the time series is approximated by that of…
Detection of blue whale vocalisations using a temporal-domain convolutional neural network
Bryan Sagredo, Sonia Español-Jiménez, Felipe Tobar
We present a framework for detecting blue whale vocalisations from acoustic submarine recordings. The proposed methodology comprises three stages: i) a preprocessing step where the…
Late reverberation suppression using U-nets
Diego León, Felipe Tobar
In real-world settings, speech signals are almost always affected by reverberation produced by the working environment; these corrupted signals need to be \emph{dereverberated} pri…
A novel notion of barycenter for probability distributions based on optimal weak mass transport
Elsa Cazelles, Felipe Tobar, Joaquín Fontbona
We introduce weak barycenters of a family of probability distributions, based on the recently developed notion of optimal weak transport of mass by Gozlanet al. (2017) and Backhoff…
Data Science for Engineers: A Teaching Ecosystem
Felipe Tobar, Felipe Bravo-Marquez, Jocelyn Dunstan +4
We describe an ecosystem for teaching data science (DS) to engineers which blends theory, methods, and applications, developed at the Faculty of Physical and Mathematical Sciences,…
Bayesian Reconstruction of Fourier Pairs
Felipe Tobar, Lerko Araya-Hernández, Pablo Huijse +1
In a number of data-driven applications such as detection of arrhythmia, interferometry or audio compression, observations are acquired indistinctly in the time or frequency domain…
Gaussian process imputation of multiple financial series
Taco de Wolff, Alejandro Cuevas, Felipe Tobar
In Financial Signal Processing, multiple time series such as financial indicators, stock prices and exchange rates are strongly coupled due to their dependence on the latent state…
MOGPTK: The Multi-Output Gaussian Process Toolkit
Taco de Wolff, Alejandro Cuevas, Felipe Tobar
We present MOGPTK, a Python package for multi-channel data modelling using Gaussian processes (GP). The aim of this toolkit is to make multi-output GP (MOGP) models accessible to r…
The Wasserstein-Fourier Distance for Stationary Time Series
Elsa Cazelles, Arnaud Robert, Felipe Tobar
We propose the Wasserstein-Fourier (WF) distance to measure the (dis)similarity between time series by quantifying the displacement of their energy across frequencies. The WF dista…
Band-Limited Gaussian Processes: The Sinc Kernel
Felipe Tobar
We propose a novel class of Gaussian processes (GPs) whose spectra have compact support, meaning that their sample trajectories are almost-surely band limited. As a complement to t…
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…
Low-pass filtering as Bayesian inference
Cristobal Valenzuela, Felipe Tobar
We propose a Bayesian nonparametric method for low-pass filtering that can naturally handle unevenly-sampled and noise-corrupted observations. The proposed model is constructed as…
Bayesian Nonparametric Spectral Estimation
Felipe Tobar
Spectral estimation (SE) aims to identify how the energy of a signal (e.g., a time series) is distributed across different frequencies. This can become particularly challenging whe…
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…
Spectral Mixture Kernels for Multi-Output Gaussian Processes
Gabriel Parra, Felipe Tobar
Early approaches to multiple-output Gaussian processes (MOGPs) relied on linear combinations of independent, latent, single-output Gaussian processes (GPs). This resulted in cross-…
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…