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06/01/2023
Spatio-Angular Convolutions for Super-resolution in Diffusion MRI
Diffusion MRI (dMRI) is a widely used imaging modality, but requires lon...
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06/17/2022
Shallow and Deep Nonparametric Convolutions for Gaussian Processes
A key challenge in the practical application of Gaussian processes (GPs)...
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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...
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02/09/2022
Adjoint-aided inference of Gaussian process driven differential equations
Linear systems occur throughout engineering and the sciences, most notab...
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10/26/2021
Modular Gaussian Processes for Transfer Learning
We present a framework for transfer learning based on modular variationa...
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06/10/2021
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features
Effectively modeling phenomena present in highly nonlinear dynamical sys...
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06/10/2021
Learning Nonparametric Volterra Kernels with Gaussian Processes
This paper introduces a method for the nonparametric Bayesian learning o...
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10/06/2020
Recyclable Gaussian Processes
We present a new framework for recycling independent variational approxi...
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11/28/2019
Machine Learning for a Low-cost Air Pollution Network
Data collection in economically constrained countries often necessitates...
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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...
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10/31/2019
Continual Multi-task Gaussian Processes
We address the problem of continual learning in multi-task Gaussian proc...
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09/19/2019
Adversarial Vulnerability Bounds for Gaussian Process Classification
Machine learning (ML) classification is increasingly used in safety-crit...
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09/19/2019
Differentially Private Regression and Classification with Sparse Gaussian Processes
A continuing challenge for machine learning is providing methods to perf...
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06/22/2019
Multi-task Learning for Aggregated Data using Gaussian Processes
Aggregated data is commonplace in areas such as epidemiology and demogra...
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06/21/2019
Variational Bridge Constructs for Grey Box Modelling with Gaussian Processes
This paper introduces a method for inference of heterogeneous dynamical ...
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01/07/2019
Variational bridge constructs for approximate Gaussian process regression
This paper introduces a method to approximate Gaussian process regressio...
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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 ...
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10/10/2018
Non-linear process convolutions for multi-output Gaussian processes
The paper introduces a non-linear version of the process convolution for...
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09/06/2018
Gaussian Process Regression for Binned Data
Many datasets are in the form of tables of binned data. Performing regre...
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08/29/2018
Physically-inspired Gaussian processes for transcriptional regulation in Drosophila melanogaster
The regulatory process in Drosophila melanogaster is thoroughly studied ...
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05/19/2018
Heterogeneous Multi-output Gaussian Process Prediction
We present a novel extension of multi-output Gaussian processes for hand...
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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...
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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...
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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...
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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...
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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...
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06/25/2016
Generalized Wishart processes for interpolation over diffusion tensor fields
Diffusion Magnetic Resonance Imaging (dMRI) is a non-invasive tool for w...
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03/16/2016
Short-term time series prediction using Hilbert space embeddings of autoregressive processes
Linear autoregressive models serve as basic representations of discrete ...
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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...
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11/23/2015
Sparse Linear Models applied to Power Quality Disturbance Classification
Power quality (PQ) analysis describes the non-pure electric signals that...
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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...
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03/22/2015
Indian Buffet process for model selection in convolved multiple-output Gaussian processes
Multi-output Gaussian processes have received increasing attention durin...
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02/07/2015
Discriminative training for Convolved Multiple-Output Gaussian processes
Multi-output Gaussian processes (MOGP) are probability distributions ove...
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07/13/2011
Linear Latent Force Models using Gaussian Processes
Purely data driven approaches for machine learning present difficulties ...
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06/30/2011
Kernels for Vector-Valued Functions: a Review
Kernel methods are among the most popular techniques in machine learning...
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12/16/2009
Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes
Interest in multioutput kernel methods is increasing, whether under the ...
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11/26/2009