
Compositional Modeling of Nonlinear Dynamical Systems with ODEbased Random Features
Effectively modeling phenomena present in highly nonlinear dynamical sys...
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Learning Nonparametric Volterra Kernels with Gaussian Processes
This paper introduces a method for the nonparametric Bayesian learning o...
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Recyclable Gaussian Processes
We present a new framework for recycling independent variational approxi...
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Machine Learning for a Lowcost Air Pollution Network
Data collection in economically constrained countries often necessitates...
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A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous MultiOutput Gaussian Process Model
A recent novel extension of multioutput Gaussian processes handles hete...
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Continual Multitask Gaussian Processes
We address the problem of continual learning in multitask Gaussian proc...
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Adversarial Vulnerability Bounds for Gaussian Process Classification
Machine learning (ML) classification is increasingly used in safetycrit...
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Differentially Private Regression and Classification with Sparse Gaussian Processes
A continuing challenge for machine learning is providing methods to perf...
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Multitask Learning for Aggregated Data using Gaussian Processes
Aggregated data is commonplace in areas such as epidemiology and demogra...
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Variational Bridge Constructs for Grey Box Modelling with Gaussian Processes
This paper introduces a method for inference of heterogeneous dynamical ...
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Variational bridge constructs for approximate Gaussian process regression
This paper introduces a method to approximate Gaussian process regressio...
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Sparse Gaussian process Audio Source Separation Using Spectrum Priors in the TimeDomain
Gaussian process (GP) audio source separation is a timedomain approach ...
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Nonlinear process convolutions for multioutput Gaussian processes
The paper introduces a nonlinear version of the process convolution for...
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Gaussian Process Regression for Binned Data
Many datasets are in the form of tables of binned data. Performing regre...
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Physicallyinspired Gaussian processes for transcriptional regulation in Drosophila melanogaster
The regulatory process in Drosophila melanogaster is thoroughly studied ...
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Heterogeneous Multioutput Gaussian Process Prediction
We present a novel extension of multioutput Gaussian processes for hand...
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Fast Kernel Approximations for Latent Force Models and Convolved MultipleOutput Gaussian processes
A latent force model is a Gaussian process with a covariance function in...
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Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems
This paper is concerned with estimation and stochastic control in physic...
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Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
Often in machine learning, data are collected as a combination of multip...
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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...
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A Tucker decomposition process for probabilistic modeling of diffusion magnetic resonance imaging
Diffusion magnetic resonance imaging (dMRI) is an emerging medical techn...
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Generalized Wishart processes for interpolation over diffusion tensor fields
Diffusion Magnetic Resonance Imaging (dMRI) is a noninvasive tool for w...
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Shortterm time series prediction using Hilbert space embeddings of autoregressive processes
Linear autoregressive models serve as basic representations of discrete ...
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Switched latent force models for reverseengineering transcriptional regulation in gene expression data
To survive environmental conditions, cells transcribe their response act...
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Sparse Linear Models applied to Power Quality Disturbance Classification
Power quality (PQ) analysis describes the nonpure electric signals that...
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A Parzenbased distance between probability measures as an alternative of summary statistics in Approximate Bayesian Computation
Approximate Bayesian Computation (ABC) are likelihoodfree Monte Carlo m...
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Indian Buffet process for model selection in convolved multipleoutput Gaussian processes
Multioutput Gaussian processes have received increasing attention durin...
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Discriminative training for Convolved MultipleOutput Gaussian processes
Multioutput Gaussian processes (MOGP) are probability distributions ove...
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Linear Latent Force Models using Gaussian Processes
Purely data driven approaches for machine learning present difficulties ...
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Kernels for VectorValued Functions: a Review
Kernel methods are among the most popular techniques in machine learning...
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Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes
Interest in multioutput kernel methods is increasing, whether under the ...
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Sparse Convolved Multiple Output Gaussian Processes
Recently there has been an increasing interest in methods that deal with...
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Mauricio A. Álvarez
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Lecturer in Machine Learning abd MSc Admissions Tutor for Data Analytics, and Computer Science with Speech and Language Processing.