Search Results for author: Alessio Benavoli

Found 13 papers, 4 papers with code

Bayesian Optimisation for Sequential Experimental Design with Applications in Additive Manufacturing

no code implementations27 Jul 2021 Mimi Zhang, Andrew Parnell, Dermot Brabazon, Alessio Benavoli

Bayesian optimization (BO) is an approach to globally optimizing black-box objective functions that are expensive to evaluate.

Bayesian Optimisation

Bayesian Kernelised Test of (In)dependence with Mixed-type Variables

no code implementations9 May 2021 Alessio Benavoli, Cassio de Campos

A fundamental task in AI is to assess (in)dependence between mixed-type variables (text, image, sound).

A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes

no code implementations12 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.

Active Learning Gaussian Processes +1

Time series forecasting with Gaussian Processes needs priors

1 code implementation17 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.

Gaussian Processes Time Series +1

Preferential Bayesian optimisation with Skew Gaussian Processes

no code implementations15 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.

Bayesian Optimisation Gaussian Processes +1

Orthogonally Decoupled Variational Fourier Features

no code implementations13 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.

Gaussian Processes

Skew Gaussian Processes for Classification

no code implementations26 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.

Classification Gaussian Processes +1

Recursive Estimation for Sparse Gaussian Process Regression

1 code implementation28 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.

Gaussian Processes

Statistical comparison of classifiers through Bayesian hierarchical modelling

1 code implementation28 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).

Two-sample testing

Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis

1 code implementation14 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.

State Space representation of non-stationary Gaussian Processes

no code implementations7 Jan 2016 Alessio Benavoli, Marco Zaffalon

The state space (SS) representation of Gaussian processes (GP) has recently gained a lot of interest.

Gaussian Processes

Should we really use post-hoc tests based on mean-ranks?

no code implementations9 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.

On the Complexity of Strong and Epistemic Credal Networks

no code implementations26 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.

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