research
∙
08/14/2023
Greedy online change point detection
Standard online change point detection (CPD) methods tend to have large ...
share
research
∙
05/08/2023
Gaussian process deconvolution
Let us consider the deconvolution problem, that is, to recover a latent ...
research
∙
10/11/2022
Computationally-efficient initialisation of GPs: The generalised variogram method
We present a computationally-efficient strategy to find the hyperparamet...
research
∙
02/18/2022
Nonstationary multi-output Gaussian processes via harmonizable spectral mixtures
Kernel design for Multi-output Gaussian Processes (MOGP) has received in...
research
∙
12/30/2021
Studying the Interplay between Information Loss and Operation Loss in Representations for Classification
Information-theoretic measures have been widely adopted in the design of...
research
∙
10/05/2021
Detection of blue whale vocalisations using a temporal-domain convolutional neural network
We present a framework for detecting blue whale vocalisations from acous...
research
∙
10/05/2021
Late reverberation suppression using U-nets
In real-world settings, speech signals are almost always affected by rev...
research
∙
02/26/2021
Streaming computation of optimal weak transport barycenters
We introduce the weak barycenter of a family of probability distribution...
research
∙
01/14/2021
Data Science for Engineers: A Teaching Ecosystem
We describe an ecosystem for teaching data science (DS) to engineers whi...
research
∙
11/09/2020
Bayesian Reconstruction of Fourier Pairs
In a number of data-driven applications such as detection of arrhythmia,...
research
∙
02/11/2020
Gaussian process imputation of multiple financial series
In Financial Signal Processing, multiple time series such as financial i...
research
∙
02/09/2020
MOGPTK: The Multi-Output Gaussian Process Toolkit
We present MOGPTK, a Python package for multi-channel data modelling usi...
research
∙
12/11/2019
The Wasserstein-Fourier Distance for Stationary Time Series
We introduce a novel framework for analysing stationary time series base...
research
∙
09/16/2019
Band-Limited Gaussian Processes: The Sinc Kernel
We propose a novel class of Gaussian processes (GPs) whose spectra have ...
research
∙
06/23/2019
Compositionally-Warped Gaussian Processes
The Gaussian process (GP) is a nonparametric prior distribution over fun...
research
∙
02/09/2019
Low-pass filtering as Bayesian inference
We propose a Bayesian nonparametric method for low-pass filtering that c...
research
∙
09/06/2018
Bayesian Nonparametric Spectral Estimation
Spectral estimation (SE) aims to identify how the energy of a signal (e....
research
∙
05/28/2018
Bayesian Learning with Wasserstein Barycenters
In this work we introduce a novel paradigm for Bayesian learning based o...
research
∙
03/19/2018
Learning non-Gaussian Time Series using the Box-Cox Gaussian Process
Gaussian processes (GPs) are Bayesian nonparametric generative models th...
research
∙
09/05/2017
Spectral Mixture Kernels for Multi-Output Gaussian Processes
Early approaches to multiple-output Gaussian processes (MOGPs) relied on...
research
∙
07/19/2017
Recovering Latent Signals from a Mixture of Measurements using a Gaussian Process Prior
In sensing applications, sensors cannot always measure the latent quanti...
research
∙
07/13/2017
Improving Sparsity in Kernel Adaptive Filters Using a Unit-Norm Dictionary
Kernel adaptive filters, a class of adaptive nonlinear time-series model...
research
∙
07/11/2017
