48 citations · 153 across the 35 of their papers we have counts for
7 papers · 1 filter
Sparse Gaussian Process Audio Source Separation Using Spectrum Priors in the Time-Domain
Pablo A. Alvarado, Mauricio A. Álvarez, Dan Stowell
Gaussian process (GP) audio source separation is a time-domain approach that circumvents the inherent phase approximation issue of spectrogram based methods. Furthermore, through i…
Non-linear process convolutions for multi-output Gaussian processes
Mauricio A. Álvarez, Wil O. C. Ward, Cristian Guarnizo
The paper introduces a non-linear version of the process convolution formalism for building covariance functions for multi-output Gaussian processes. The non-linearity is introduce…
Gaussian Process Regression for Binned Data
Michael Thomas Smith, Mauricio A Alvarez, Neil D Lawrence
Many datasets are in the form of tables of binned data. Performing regression on these data usually involves either reading off bin heights, ignoring data from neighbouring bins or…
Physically-Inspired Gaussian Process Models for Post-Transcriptional Regulation in Drosophila
Andrés F. López-Lopera, Nicolas Durrande, Mauricio A. Alvarez
The regulatory process of Drosophila is thoroughly studied for understanding a great variety of biological principles. While pattern-forming gene networks are analysed in the trans…
Bayesian inversion of a diffusion evolution equation with application to Biology
Jean-Charles Croix, Nicolas Durrande, Mauricio Alvarez
A common task in experimental sciences is to fit mathematical models to real-world measurements to improve understanding of natural phenomenon (reverse-engineering or inverse model…
Heterogeneous Multi-output Gaussian Process Prediction
Pablo Moreno-Muñoz, Antonio Artés-Rodríguez, Mauricio A. Álvarez
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-valu…
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processes
Cristian Guarnizo, Mauricio A. Álvarez
A latent force model is a Gaussian process with a covariance function inspired by a differential operator. Such covariance function is obtained by performing convolution integrals…