142 citations · 600 across the 47 of their papers we have counts for
7 papers · 1 filter
All You Need is a Good Functional Prior for Bayesian Deep Learning
Ba-Hien Tran, Simone Rossi, Dimitrios Milios +1
The Bayesian treatment of neural networks dictates that a prior distribution is specified over their weight and bias parameters. This poses a challenge because modern neural networ…
Sparse within Sparse Gaussian Processes using Neighbor Information
Gia-Lac Tran, Dimitrios Milios, Pietro Michiardi +1
Approximations to Gaussian processes based on inducing variables, combined with variational inference techniques, enable state-of-the-art sparse approaches to infer GPs at scale th…
An Identifiable Double VAE For Disentangled Representations
Graziano Mita, Maurizio Filippone, Pietro Michiardi
A large part of the literature on learning disentangled representations focuses on variational autoencoders (VAE). Recent developments demonstrate that disentanglement cannot be ob…
Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling
Giulio Franzese, Rosa Candela, Dimitrios Milios +2
In this work we define a unified mathematical framework to deepen our understanding of the role of stochastic gradient (SG) noise on the behavior of Markov chain Monte Carlo sampli…
A Variational View on Bootstrap Ensembles as Bayesian Inference
Dimitrios Milios, Pietro Michiardi, Maurizio Filippone
In this paper, we employ variational arguments to establish a connection between ensemble methods for Neural Networks and Bayesian inference. We consider an ensemble-based scheme w…
Model Monitoring and Dynamic Model Selection in Travel Time-series Forecasting
Rosa Candela, Pietro Michiardi, Maurizio Filippone +1
Accurate travel products price forecasting is a highly desired feature that allows customers to take informed decisions about purchases, and companies to build and offer attractive…
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations
Simone Rossi, Markus Heinonen, Edwin V. Bonilla +2
Variational inference techniques based on inducing variables provide an elegant framework for scalable posterior estimation in Gaussian process (GP) models. Besides enabling scalab…