48 citations · 153 across the 35 of their papers we have counts for
41 papers
Latent Variable Multi-output Gaussian Processes for Hierarchical Datasets
Chunchao Ma, Arthur Leroy, Mauricio Alvarez
Multi-output Gaussian processes (MOGPs) have been introduced to deal with multiple tasks by exploiting the correlations between different outputs. Generally, MOGPs models assume a…
Spatio-Angular Convolutions for Super-resolution in Diffusion MRI
Matthew Lyon, Paul Armitage, Mauricio A Álvarez
Diffusion MRI (dMRI) is a widely used imaging modality, but requires long scanning times to acquire high resolution datasets. By leveraging the unique geometry present within this…
Shallow and Deep Nonparametric Convolutions for Gaussian Processes
Thomas M. McDonald, Magnus Ross, Michael T. Smith +1
A key challenge in the practical application of Gaussian processes (GPs) is selecting a proper covariance function. The moving average, or process convolutions, construction of GPs…
Modelling calibration uncertainty in networks of environmental sensors
Michael Thomas Smith, Magnus Ross, Joel Ssematimba +4
Networks of low-cost sensors are becoming ubiquitous, but often suffer from poor accuracies and drift. Regular colocation with reference sensors allows recalibration but is complic…
Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional Autoencoder
Matthew Lyon, Paul Armitage, Mauricio A. Álvarez
High resolution diffusion MRI (dMRI) data is often constrained by limited scanning time in clinical settings, thus restricting the use of downstream analysis techniques that would…
Adjoint-aided inference of Gaussian process driven differential equations
Paterne Gahungu, Christopher W Lanyon, Mauricio A Alvarez +3
Linear systems occur throughout engineering and the sciences, most notably as differential equations. In many cases the forcing function for the system is unknown, and interest lie…
Modular Gaussian Processes for Transfer Learning
Pablo Moreno-Muñoz, Antonio Artés-Rodríguez, Mauricio A. Álvarez
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, one…
Learning Nonparametric Volterra Kernels with Gaussian Processes
Magnus Ross, Michael T. Smith, Mauricio A. Álvarez
This paper introduces a method for the nonparametric Bayesian learning of nonlinear operators, through the use of the Volterra series with kernels represented using Gaussian proces…
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features
Thomas M. McDonald, Mauricio A. Álvarez
Effectively modeling phenomena present in highly nonlinear dynamical systems whilst also accurately quantifying uncertainty is a challenging task, which often requires problem-spec…
Recyclable Gaussian Processes
Pablo Moreno-Muñoz, Antonio Artés-Rodríguez, Mauricio A. Álvarez
We present a new framework for recycling independent variational approximations to Gaussian processes. The main contribution is the construction of variational ensembles given a di…
Multi-task Causal Learning with Gaussian Processes
Virginia Aglietti, Theodoros Damoulas, Mauricio Álvarez +1
This paper studies the problem of learning the correlation structure of a set of intervention functions defined on the directed acyclic graph (DAG) of a causal model. This is usefu…
Machine Learning for a Low-cost Air Pollution Network
Michael T. Smith, Joel Ssematimba, Mauricio A. Alvarez +1
Data collection in economically constrained countries often necessitates using approximate and biased measurements due to the low-cost of the sensors used. This leads to potentiall…
A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multi-Output Gaussian Process Model
Juan-José Giraldo, Mauricio A. Álvarez
A recent novel extension of multi-output Gaussian processes handles heterogeneous outputs assuming that each output has its own likelihood function. It uses a vector-valued Gaussia…
Continual Multi-task Gaussian Processes
Pablo Moreno-Muñoz, Antonio Artés-Rodríguez, Mauricio A. Álvarez
We address the problem of continual learning in multi-task Gaussian process (GP) models for handling sequential input-output observations. Our approach extends the existing prior-p…
Differentially Private Regression and Classification with Sparse Gaussian Processes
Michael Thomas Smith, Mauricio A. Alvarez, Neil D. Lawrence
A continuing challenge for machine learning is providing methods to perform computation on data while ensuring the data remains private. In this paper we build on the provable priv…
Adversarial Vulnerability Bounds for Gaussian Process Classification
Michael Thomas Smith, Kathrin Grosse, Michael Backes +1
Machine learning (ML) classification is increasingly used in safety-critical systems. Protecting ML classifiers from adversarial examples is crucial. We propose that the main threa…
Multi-task Learning for Aggregated Data using Gaussian Processes
Fariba Yousefi, Michael Thomas Smith, Mauricio A. Álvarez
Aggregated data is commonplace in areas such as epidemiology and demography. For example, census data for a population is usually given as averages defined over time periods or spa…
Black-Box Inference for Non-Linear Latent Force Models
Wil O. C. Ward, Tom Ryder, Dennis Prangle +1
Latent force models are systems whereby there is a mechanistic model describing the dynamics of the system state, with some unknown forcing term that is approximated with a Gaussia…
Variational bridge constructs for approximate Gaussian process regression
Wil O C Ward, Mauricio A Álvarez
This paper introduces a method to approximate Gaussian process regression by representing the problem as a stochastic differential equation and using variational inference to appro…
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…