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02/01/2023
Learning Choice Functions with Gaussian Processes
In consumer theory, ranking available objects by means of preference rel...
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08/04/2022
Credal Valuation Networks for Machine Reasoning Under Uncertainty
Contemporary undertakings provide limitless opportunities for widespread...
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03/08/2022
Why we should interpret density matrices as moment matrices: the case of (in)distinguishable particles and the emergence of classical reality
We introduce a formulation of quantum theory (QT) as a general probabili...
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12/17/2021
Correlated Product of Experts for Sparse Gaussian Process Regression
Gaussian processes (GPs) are an important tool in machine learning and s...
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10/15/2021
Choice functions based multi-objective Bayesian optimisation
In this work we introduce a new framework for multi-objective Bayesian o...
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09/28/2021
Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect
We present a two-stage Metropolis-Hastings algorithm for sampling probab...
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07/27/2021
Bayesian Optimisation for Sequential Experimental Design with Applications in Additive Manufacturing
Bayesian optimization (BO) is an approach to globally optimizing black-b...
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05/09/2021
Bayesian Kernelised Test of (In)dependence with Mixed-type Variables
A fundamental task in AI is to assess (in)dependence between mixed-type ...
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12/12/2020
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes
Skew-Gaussian processes (SkewGPs) extend the multivariate Unified Skew-N...
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09/17/2020
Automatic Forecasting using Gaussian Processes
Automatic forecasting is the task of receiving a time series and returni...
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08/15/2020
Preferential Bayesian optimisation with Skew Gaussian Processes
Bayesian optimisation (BO) is a very effective approach for sequential b...
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07/13/2020
Orthogonally Decoupled Variational Fourier Features
Sparse inducing points have long been a standard method to fit Gaussian ...
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05/26/2020
Skew Gaussian Processes for Classification
Gaussian processes (GPs) are distributions over functions, which provide...
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05/28/2019
Recursive Estimation for Sparse Gaussian Process Regression
Gaussian Processes (GPs) are powerful kernelized methods for non-paramet...
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02/12/2019
Computational Complexity and the Nature of Quantum Mechanics
Quantum theory (QT) has been confirmed by numerous experiments, yet we s...
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02/09/2019
Computational Complexity and the Nature of Quantum Mechanics (Extended version)
Quantum theory (QT) has been confirmed by numerous experiments, yet we s...
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09/28/2016
Statistical comparison of classifiers through Bayesian hierarchical modelling
Usually one compares the accuracy of two competing classifiers via null ...
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06/14/2016
Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis
The machine learning community adopted the use of null hypothesis signif...
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01/07/2016
State Space representation of non-stationary Gaussian Processes
The state space (SS) representation of Gaussian processes (GP) has recen...
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05/09/2015
Should we really use post-hoc tests based on mean-ranks?
The statistical comparison of multiple algorithms over multiple data set...
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09/26/2013
