90 citations · 514 across the 54 of their papers we have counts for
9 papers · 1 filter
dynoGP: Deep Gaussian Processes for dynamic system identification
Alessio Benavoli, Dario Piga, Marco Forgione +1
In this work, we present a novel approach to system identification for dynamical systems, based on a specific class of Deep Gaussian Processes (Deep GPs). These models are construc…
A Note on Bayesian Networks with Latent Root Variables
Marco Zaffalon, Alessandro Antonucci
We characterise the likelihood function computed from a Bayesian network with latent variables as root nodes. We show that the marginal distribution over the remaining, manifest, v…
Bounding Counterfactuals under Selection Bias
Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas +2
Causal analysis may be affected by selection bias, which is defined as the systematic exclusion of data from a certain subpopulation. Previous work in this area focused on the deri…
Correlated Product of Experts for Sparse Gaussian Process Regression
Manuel Schürch, Dario Azzimonti, Alessio Benavoli +1
Gaussian processes (GPs) are an important tool in machine learning and statistics with applications ranging from social and natural science through engineering. They constitute a p…
Time series forecasting with Gaussian Processes needs priors
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 (GPs) are a powerfu…
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…
Recursive Estimation for Sparse Gaussian Process Regression
Manuel Schürch, Dario Azzimonti, Alessio Benavoli +1
Gaussian Processes (GPs) are powerful kernelized methods for non-parameteric regression used in many applications. However, their use is limited to a few thousand of training sampl…
Hierarchical Multinomial-Dirichlet model for the estimation of conditional probability tables
L. Azzimonti, G. Corani, M. Zaffalon
We present a novel approach for estimating conditional probability tables, based on a joint, rather than independent, estimate of the conditional distributions belonging to the sam…
Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis
Alessio Benavoli, Giorgio Corani, Janez Demsar +1
The machine learning community adopted the use of null hypothesis significance testing (NHST) in order to ensure the statistical validity of results. Many scientific fields however…