Mauricio A. Álvarez

research

∙ 06/01/2023

Spatio-Angular Convolutions for Super-resolution in Diffusion MRI

Diffusion MRI (dMRI) is a widely used imaging modality, but requires lon...

0 Matthew Lyon, et al. ∙

research

∙ 06/17/2022

Shallow and Deep Nonparametric Convolutions for Gaussian Processes

A key challenge in the practical application of Gaussian processes (GPs)...

0 Thomas M. McDonald, et al. ∙

research

∙ 03/29/2022

Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional Autoencoder

High resolution diffusion MRI (dMRI) data is often constrained by limite...

0 Matthew Lyon, et al. ∙

research

∙ 02/09/2022

Adjoint-aided inference of Gaussian process driven differential equations

Linear systems occur throughout engineering and the sciences, most notab...

11 Paterne Gahungu, et al. ∙

research

∙ 10/26/2021

Modular Gaussian Processes for Transfer Learning

We present a framework for transfer learning based on modular variationa...

10 Pablo Moreno-Muñoz, et al. ∙

research

∙ 06/10/2021

Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features

Effectively modeling phenomena present in highly nonlinear dynamical sys...

0 Thomas M. McDonald, et al. ∙

research

∙ 06/10/2021

Learning Nonparametric Volterra Kernels with Gaussian Processes

This paper introduces a method for the nonparametric Bayesian learning o...

0 Magnus Ross, et al. ∙

research

∙ 10/06/2020

Recyclable Gaussian Processes

We present a new framework for recycling independent variational approxi...

6 Pablo Moreno-Muñoz, et al. ∙

research

∙ 11/28/2019

Machine Learning for a Low-cost Air Pollution Network

Data collection in economically constrained countries often necessitates...

9 Michael T. Smith, et al. ∙

research

∙ 11/22/2019

A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multi-Output Gaussian Process Model

A recent novel extension of multi-output Gaussian processes handles hete...

12 Juan-José Giraldo, et al. ∙

research

∙ 10/31/2019

Continual Multi-task Gaussian Processes

We address the problem of continual learning in multi-task Gaussian proc...

16 Pablo Moreno-Muñoz, et al. ∙

research

∙ 09/19/2019

Adversarial Vulnerability Bounds for Gaussian Process Classification

Machine learning (ML) classification is increasingly used in safety-crit...

6 Michael Thomas Smith, et al. ∙

research

∙ 09/19/2019

Differentially Private Regression and Classification with Sparse Gaussian Processes

A continuing challenge for machine learning is providing methods to perf...

12 Michael Thomas Smith, et al. ∙

research

∙ 06/22/2019

Multi-task Learning for Aggregated Data using Gaussian Processes

Aggregated data is commonplace in areas such as epidemiology and demogra...

4 Fariba Yousefi, et al. ∙

research

∙ 06/21/2019

Variational Bridge Constructs for Grey Box Modelling with Gaussian Processes

This paper introduces a method for inference of heterogeneous dynamical ...

5 Wil O. C. Ward, et al. ∙

research

∙ 01/07/2019

Variational bridge constructs for approximate Gaussian process regression

This paper introduces a method to approximate Gaussian process regressio...

8 Wil O. C. Ward, et al. ∙

research

∙ 10/30/2018

Sparse Gaussian process Audio Source Separation Using Spectrum Priors in the Time-Domain

Gaussian process (GP) audio source separation is a time-domain approach ...

2 Pablo A. Alvarado, et al. ∙

research

∙ 10/10/2018

Non-linear process convolutions for multi-output Gaussian processes

The paper introduces a non-linear version of the process convolution for...

10 Mauricio A. Álvarez, et al. ∙

research

∙ 09/06/2018

Gaussian Process Regression for Binned Data

Many datasets are in the form of tables of binned data. Performing regre...

0 Michael Thomas Smith, et al. ∙

research

∙ 08/29/2018

Physically-inspired Gaussian processes for transcriptional regulation in Drosophila melanogaster

The regulatory process in Drosophila melanogaster is thoroughly studied ...

12 Andrés F. López-Lopera, et al. ∙

research

∙ 05/19/2018

Heterogeneous Multi-output Gaussian Process Prediction

We present a novel extension of multi-output Gaussian processes for hand...

0 Pablo Moreno-Muñoz, et al. ∙

research

∙ 05/18/2018

Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processes

A latent force model is a Gaussian process with a covariance function in...

0 Cristian Guarnizo, et al. ∙

research

∙ 09/15/2017

Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems

This paper is concerned with estimation and stochastic control in physic...

0 Simo Särkkä, et al. ∙

research

∙ 05/27/2017

Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes

Often in machine learning, data are collected as a combination of multip...

0 Zhenwen Dai, et al. ∙

research

∙ 08/17/2016

A Three Spatial Dimension Wave Latent Force Model for Describing Excitation Sources and Electric Potentials Produced by Deep Brain Stimulation

Deep brain stimulation (DBS) is a surgical treatment for Parkinson's Dis...

0 Pablo A. Alvarado, et al. ∙

research

∙ 06/25/2016

A Tucker decomposition process for probabilistic modeling of diffusion magnetic resonance imaging

Diffusion magnetic resonance imaging (dMRI) is an emerging medical techn...

0 Hernan Dario Vargas Cardona, et al. ∙

research

∙ 06/25/2016

Generalized Wishart processes for interpolation over diffusion tensor fields

Diffusion Magnetic Resonance Imaging (dMRI) is a non-invasive tool for w...

0 Hernan Dario Vargas Cardona, et al. ∙

research

∙ 03/16/2016

Short-term time series prediction using Hilbert space embeddings of autoregressive processes

Linear autoregressive models serve as basic representations of discrete ...

0 Edgar A. Valencia, et al. ∙

research

∙ 11/23/2015

Switched latent force models for reverse-engineering transcriptional regulation in gene expression data

To survive environmental conditions, cells transcribe their response act...

0 Andrés F. López-Lopera, et al. ∙

research

∙ 11/23/2015

Sparse Linear Models applied to Power Quality Disturbance Classification

Power quality (PQ) analysis describes the non-pure electric signals that...

0 Andrés F. López-Lopera, et al. ∙

research

∙ 03/30/2015

A Parzen-based distance between probability measures as an alternative of summary statistics in Approximate Bayesian Computation

Approximate Bayesian Computation (ABC) are likelihood-free Monte Carlo m...

0 Carlos D. Zuluaga, et al. ∙

research

∙ 03/22/2015

Indian Buffet process for model selection in convolved multiple-output Gaussian processes

Multi-output Gaussian processes have received increasing attention durin...

0 Cristian Guarnizo, et al. ∙

research

∙ 02/07/2015

Discriminative training for Convolved Multiple-Output Gaussian processes

Multi-output Gaussian processes (MOGP) are probability distributions ove...

0 Sebastián Gómez-González, et al. ∙

research

∙ 07/13/2011

Linear Latent Force Models using Gaussian Processes

Purely data driven approaches for machine learning present difficulties ...

0 Mauricio A. Álvarez, et al. ∙

research

∙ 06/30/2011

Kernels for Vector-Valued Functions: a Review

Kernel methods are among the most popular techniques in machine learning...

0 Mauricio A. Álvarez, et al. ∙

research

∙ 12/16/2009

Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes

Interest in multioutput kernel methods is increasing, whether under the ...

0 Mauricio A. Álvarez, et al. ∙

research

∙ 11/26/2009

Sparse Convolved Multiple Output Gaussian Processes

Recently there has been an increasing interest in methods that deal with...

0 Mauricio A. Álvarez, et al. ∙

Mauricio A. Álvarez

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