
Automatic Forecasting using Gaussian Processes
Automatic forecasting is the task of receiving a time series and returni...
read it

Preferential Bayesian optimisation with Skew Gaussian Processes
Bayesian optimisation (BO) is a very effective approach for sequential b...
read it

Orthogonally Decoupled Variational Fourier Features
Sparse inducing points have long been a standard method to fit Gaussian ...
read it

Skew Gaussian Processes for Classification
Gaussian processes (GPs) are distributions over functions, which provide...
read it

Recursive Estimation for Sparse Gaussian Process Regression
Gaussian Processes (GPs) are powerful kernelized methods for nonparamet...
read it

Computational Complexity and the Nature of Quantum Mechanics
Quantum theory (QT) has been confirmed by numerous experiments, yet we s...
read it

Computational Complexity and the Nature of Quantum Mechanics (Extended version)
Quantum theory (QT) has been confirmed by numerous experiments, yet we s...
read it

Statistical comparison of classifiers through Bayesian hierarchical modelling
Usually one compares the accuracy of two competing classifiers via null ...
read it

Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis
The machine learning community adopted the use of null hypothesis signif...
read it

State Space representation of nonstationary Gaussian Processes
The state space (SS) representation of Gaussian processes (GP) has recen...
read it

Should we really use posthoc tests based on meanranks?
The statistical comparison of multiple algorithms over multiple data set...
read it

On the Complexity of Strong and Epistemic Credal Networks
Credal networks are graphbased statistical models whose parameters take...
read it
Alessio Benavoli
is this you? claim profile