Search Results for author: Dimitrios Milios

Found 10 papers, 1 papers with code

Revisiting the Effects of Stochasticity for Hamiltonian Samplers

no code implementations30 Jun 2021 Giulio Franzese, Dimitrios Milios, Maurizio Filippone, Pietro Michiardi

We revisit the theoretical properties of Hamiltonian stochastic differential equations (SDES) for Bayesian posterior sampling, and we study the two types of errors that arise from numerical SDE simulation: the discretization error and the error due to noisy gradient estimates in the context of data subsampling.

Numerical Integration

Model Selection for Bayesian Autoencoders

no code implementations NeurIPS 2021 Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone

We develop a novel method for carrying out model selection for Bayesian autoencoders (BAEs) by means of prior hyper-parameter optimization.

Model Selection Representation Learning

All You Need is a Good Functional Prior for Bayesian Deep Learning

no code implementations25 Nov 2020 Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Maurizio Filippone

This poses a challenge because modern neural networks are characterized by a large number of parameters, and the choice of these priors has an uncontrolled effect on the induced functional prior, which is the distribution of the functions obtained by sampling the parameters from their prior distribution.

Gaussian Processes

Parametric Bootstrap Ensembles as Variational Inference

no code implementations pproximateinference AABI Symposium 2021 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.

Bayesian Inference Variational Inference

Sparse within Sparse Gaussian Processes using Neighbor Information

no code implementations10 Nov 2020 Gia-Lac Tran, Dimitrios Milios, Pietro Michiardi, Maurizio Filippone

In this work, we address one limitation of sparse GPs, which is due to the challenge in dealing with a large number of inducing variables without imposing a special structure on the inducing inputs.

Gaussian Processes Variational Inference

Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling

no code implementations9 Jun 2020 Giulio Franzese, Rosa Candela, Dimitrios Milios, Maurizio Filippone, Pietro Michiardi

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 sampling (SGMCMC) algorithms.

A Variational View on Bootstrap Ensembles as Bayesian Inference

no code implementations8 Jun 2020 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.

Bayesian Inference

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