Search Results for author: Alessio Benavoli

Found 19 papers, 9 papers with code

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.

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.

BIG-bench Machine Learning

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.

BIG-bench Machine Learning Gaussian Processes

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.

BIG-bench Machine Learning

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

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 regression

Skew Gaussian Processes for Classification

1 code implementation26 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

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

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 +2

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

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

1 code implementation12 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 Binary Classification +2

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).

Vocal Bursts Type Prediction

Bayesian Optimisation for Sequential Experimental Design with Applications in Additive Manufacturing

1 code implementation27 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.

Experimental Design

Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect

no code implementations28 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.

Gaussian Processes Probabilistic Programming

Choice functions based multi-objective Bayesian optimisation

no code implementations15 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''.

Bayesian Optimisation

Correlated Product of Experts for Sparse Gaussian Process Regression

no code implementations17 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.

Gaussian Processes regression +1

Credal Valuation Networks for Machine Reasoning Under Uncertainty

no code implementations4 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.

Learning Choice Functions with Gaussian Processes

1 code implementation1 Feb 2023 Alessio Benavoli, Dario Azzimonti, Dario Piga

We propose a Gaussian Process model to learn choice functions from choice-data.

Gaussian Processes

A tutorial on learning from preferences and choices with Gaussian Processes

1 code implementation18 Mar 2024 Alessio Benavoli, Dario Azzimonti

Preference modelling lies at the intersection of economics, decision theory, machine learning and statistics.

Gaussian Processes

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