no code implementations • 7 Jan 2016 • Alessio Benavoli, Marco Zaffalon
The state space (SS) representation of Gaussian processes (GP) has recently gained a lot of interest.
no code implementations • 9 May 2015 • Alessio Benavoli, Giorgio Corani, Francesca Mangili
In other words, the outcome of the comparison between algorithms A and B depends also on the performance of the other algorithms included in the original experiment.
no code implementations • 26 Sep 2013 • Denis D. Maua, Cassio Polpo de Campos, Alessio Benavoli, Alessandro Antonucci
In this paper, we study inferential complexity under the concepts of epistemic irrelevance and strong independence.
no code implementations • 13 Jul 2020 • Dario Azzimonti, Manuel Schürch, Alessio Benavoli, Marco Zaffalon
Sparse inducing points have long been a standard method to fit Gaussian processes to big data.
no code implementations • 15 Aug 2020 • Alessio Benavoli, Dario Azzimonti, Dario Piga
In this paper, we prove that the true posterior distribution of the preference function is a Skew Gaussian Process (SkewGP), with highly skewed pairwise marginals and, thus, show that Laplace's method usually provides a very poor approximation.
no code implementations • 9 May 2021 • Alessio Benavoli, Cassio de Campos
A fundamental task in AI is to assess (in)dependence between mixed-type variables (text, image, sound).
1 code implementation • 27 Jul 2021 • Mimi Zhang, Andrew Parnell, Dermot Brabazon, Alessio Benavoli
This work aims to bring attention to the benefits of applying BO in designing experiments and to provide a BO manual, covering both methodology and software, for the convenience of anyone who wants to apply or learn BO.
no code implementations • 28 Sep 2021 • Alessio Benavoli, Jason Wyse, Arthur White
We present a two-stage Metropolis-Hastings algorithm for sampling probabilistic models, whose log-likelihood is computationally expensive to evaluate, by using a surrogate Gaussian Process (GP) model.
no code implementations • 15 Oct 2021 • Alessio Benavoli, Dario Azzimonti, Dario Piga
In this work we introduce a new framework for multi-objective Bayesian optimisation where the multi-objective functions can only be accessed via choice judgements, such as ``I pick options A, B, C among this set of five options A, B, C, D, E''.
no code implementations • 17 Dec 2021 • Manuel Schürch, Dario Azzimonti, Alessio Benavoli, Marco Zaffalon
Gaussian processes (GPs) are an important tool in machine learning and statistics with applications ranging from social and natural science through engineering.
no code implementations • 4 Aug 2022 • Branko Ristic, Alessio Benavoli, Sanjeev Arulampalam
Contemporary undertakings provide limitless opportunities for widespread application of machine reasoning and artificial intelligence in situations characterised by uncertainty, hostility and sheer volume of data.
1 code implementation • 1 Feb 2023 • Alessio Benavoli, Dario Azzimonti, Dario Piga
We propose a Gaussian Process model to learn choice functions from choice-data.
1 code implementation • 18 Mar 2024 • Alessio Benavoli, Dario Azzimonti
Preference modelling lies at the intersection of economics, decision theory, machine learning and statistics.
1 code implementation • 26 May 2020 • Alessio Benavoli, Dario Azzimonti, Dario Piga
In spite of their success, GPs have limited use in some applications, for example, in some cases a symmetric distribution with respect to its mean is an unreasonable model.
1 code implementation • 12 Dec 2020 • Alessio Benavoli, Dario Azzimonti, Dario Piga
In a recent contribution we showed that SkewGP and probit likelihood are conjugate, which allows us to compute the exact posterior for non-parametric binary classification and preference learning.
1 code implementation • 17 Sep 2020 • Giorgio Corani, Alessio Benavoli, Marco Zaffalon
Automatic forecasting is the task of receiving a time series and returning a forecast for the next time steps without any human intervention.
1 code implementation • 28 May 2019 • Manuel Schürch, Dario Azzimonti, Alessio Benavoli, Marco Zaffalon
Gaussian Processes (GPs) are powerful kernelized methods for non-parameteric regression used in many applications.
1 code implementation • 14 Jun 2016 • Alessio Benavoli, Giorgio Corani, Janez Demsar, Marco Zaffalon
The machine learning community adopted the use of null hypothesis significance testing (NHST) in order to ensure the statistical validity of results.
1 code implementation • 28 Sep 2016 • Giorgio Corani, Alessio Benavoli, Janez Demšar, Francesca Mangili, Marco Zaffalon
Usually one compares the accuracy of two competing classifiers via null hypothesis significance tests (nhst).