6 papers
Choice functions based multi-objective Bayesian optimisation
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…
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes
Alessio Benavoli, Dario Azzimonti, Dario Piga
Skew-Gaussian processes (SkewGPs) extend the multivariate Unified Skew-Normal distributions over finite dimensional vectors to distribution over functions. SkewGPs are more general…
An exact kernel framework for spatio-temporal dynamics
Oleg Szehr, Dario Azzimonti, Laura Azzimonti
A kernel-based framework for spatio-temporal data analysis is introduced that applies in situations when the underlying system dynamics are governed by a dynamic equation. The key…
Preferential Bayesian optimisation with Skew Gaussian Processes
Alessio Benavoli, Dario Azzimonti, Dario Piga
Preferential Bayesian optimisation (PBO) deals with optimisation problems where the objective function can only be accessed via preference judgments, such as "this is better than t…
Orthogonally Decoupled Variational Fourier Features
Dario Azzimonti, Manuel Schürch, Alessio Benavoli +1
Sparse inducing points have long been a standard method to fit Gaussian processes to big data. In the last few years, spectral methods that exploit approximations of the covariance…
Skew Gaussian Processes for Classification
Alessio Benavoli, Dario Azzimonti, Dario Piga
Gaussian processes (GPs) are distributions over functions, which provide a Bayesian nonparametric approach to regression and classification. In spite of their success, GPs have lim…