33 citations · 73 across the 12 of their papers we have counts for
12 papers
Model Selection for Bayesian Autoencoders
Ba-Hien Tran, Simone Rossi, Dimitrios Milios +3
We develop a novel method for carrying out model selection for Bayesian autoencoders (BAEs) by means of prior hyper-parameter optimization. Inspired by the common practice of type-…
Revisiting the Effects of Stochasticity for Hamiltonian Samplers
Giulio Franzese, Dimitrios Milios, Maurizio Filippone +1
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…
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…
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…
A Data-Driven Approach to Dynamically Adjust Resource Allocation for Compute Clusters
Francesco Pace, Dimitrios Milios, Damiano Carra +2
Nowadays, data-centers are largely under-utilized because resource allocation is based on reservation mechanisms which ignore actual resource utilization. Indeed, it is common to r…
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…
Probabilistic Model Checking for Continuous Time Markov Chains via Sequential Bayesian Inference
Dimitrios Milios, Guido Sanguinetti, David Schnoerr
Probabilistic model checking for systems with large or unbounded state space is a challenging computational problem in formal modelling and its applications. Numerical algorithms r…
Property-driven State-Space Coarsening for Continuous Time Markov Chains
Michalis Michaelides, Dimitrios Milios, Jane Hillston +1
Dynamical systems with large state-spaces are often expensive to thoroughly explore experimentally. Coarse-graining methods aim to define simpler systems which are more amenable to…
Policy learning for time-bounded reachability in Continuous-Time Markov Decision Processes via doubly-stochastic gradient ascent
Ezio Bartocci, Luca Bortolussi, Tomǎš Brázdil +2
Continuous-time Markov decision processes are an important class of models in a wide range of applications, ranging from cyber-physical systems to synthetic biology. A central prob…
Smoothed Model Checking for Uncertain Continuous Time Markov Chains
Luca Bortolussi, Dimitrios Milios, Guido Sanguinetti
We consider the problem of computing the satisfaction probability of a formula for stochastic models with parametric uncertainty. We show that this satisfaction probability is a sm…