142 citations · 600 across the 47 of their papers we have counts for
6 papers · 1 filter
Variational Calibration of Computer Models
Sébastien Marmin, Maurizio Filippone
Bayesian calibration of black-box computer models offers an established framework to obtain a posterior distribution over model parameters. Traditional Bayesian calibration involve…
Good Initializations of Variational Bayes for Deep Models
Simone Rossi, Pietro Michiardi, Maurizio Filippone
Stochastic variational inference is an established way to carry out approximate Bayesian inference for deep models. While there have been effective proposals for good initializatio…
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi +2
In this paper, we study the problem of deriving fast and accurate classification algorithms with uncertainty quantification. Gaussian process classification provides a principled a…
Calibrating Deep Convolutional Gaussian Processes
Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham +2
The wide adoption of Convolutional Neural Networks (CNNs) in applications where decision-making under uncertainty is fundamental, has brought a great deal of attention to the abili…
Constraining the Dynamics of Deep Probabilistic Models
Marco Lorenzi, Maurizio Filippone
We introduce a novel generative formulation of deep probabilistic models implementing "soft" constraints on their function dynamics. In particular, we develop a flexible methodolog…
Assessing Bayesian Nonparametric Log-Linear Models: an application to Disclosure Risk estimation
Cinzia Carota, Maurizio Filippone, Silvia Polettini
We present a method for identification of models with good predictive performances in the family of Bayesian log-linear mixed models with Dirichlet process random effects. Such a p…