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08/14/2023
Greedy online change point detection
Standard online change point detection (CPD) methods tend to have large ...
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05/08/2023
Gaussian process deconvolution
Let us consider the deconvolution problem, that is, to recover a latent ...
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10/11/2022
Computationally-efficient initialisation of GPs: The generalised variogram method
We present a computationally-efficient strategy to find the hyperparamet...
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02/18/2022
Nonstationary multi-output Gaussian processes via harmonizable spectral mixtures
Kernel design for Multi-output Gaussian Processes (MOGP) has received in...
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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...
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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...
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10/05/2021
Late reverberation suppression using U-nets
In real-world settings, speech signals are almost always affected by rev...
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02/26/2021
Streaming computation of optimal weak transport barycenters
We introduce the weak barycenter of a family of probability distribution...
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01/14/2021
Data Science for Engineers: A Teaching Ecosystem
We describe an ecosystem for teaching data science (DS) to engineers whi...
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11/09/2020
Bayesian Reconstruction of Fourier Pairs
In a number of data-driven applications such as detection of arrhythmia,...
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02/11/2020
Gaussian process imputation of multiple financial series
In Financial Signal Processing, multiple time series such as financial i...
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02/09/2020
MOGPTK: The Multi-Output Gaussian Process Toolkit
We present MOGPTK, a Python package for multi-channel data modelling usi...
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12/11/2019
The Wasserstein-Fourier Distance for Stationary Time Series
We introduce a novel framework for analysing stationary time series base...
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09/16/2019
Band-Limited Gaussian Processes: The Sinc Kernel
We propose a novel class of Gaussian processes (GPs) whose spectra have ...
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06/23/2019
Compositionally-Warped Gaussian Processes
The Gaussian process (GP) is a nonparametric prior distribution over fun...
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02/09/2019
Low-pass filtering as Bayesian inference
We propose a Bayesian nonparametric method for low-pass filtering that c...
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09/06/2018
Bayesian Nonparametric Spectral Estimation
Spectral estimation (SE) aims to identify how the energy of a signal (e....
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05/28/2018
Bayesian Learning with Wasserstein Barycenters
In this work we introduce a novel paradigm for Bayesian learning based o...
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03/19/2018
Learning non-Gaussian Time Series using the Box-Cox Gaussian Process
Gaussian processes (GPs) are Bayesian nonparametric generative models th...
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09/05/2017
Spectral Mixture Kernels for Multi-Output Gaussian Processes
Early approaches to multiple-output Gaussian processes (MOGPs) relied on...
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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...
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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...
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07/11/2017