Global Optimization of Analogy-Based Software Cost Estimation with Genetic Algorithms. - Dimitrios Milios; Ioannis Stamelos; Christos Chatzibagias (AIAI, 2011)

Compositional Approximate Markov Chain Aggregation for PEPA Models. - Dimitrios Milios; Stephen Gilmore (EPEW/UKPEW, 2012)

Stochastic Modelling of the Kai-based Circadian Clock. - Chris J. Banks; Allan Clark; Anastasis Georgoulas; Stephen Gilmore; Jane Hillston; Dimitrios Milios; Ian Stark (PASM/PDMC, 2012)

Markov Chain Simulation with Fewer Random Samples. - Dimitrios Milios; Stephen Gilmore (PASM/PDMC, 2012)

A genetic algorithm approach to global optimization of software cost estimation by analogy. - Dimitrios Milios; Ioannis Stamelos; Christos Chatzibagias (Intell. Decis. Technol., 2013)

Probabilistic Programming Process Algebra. - Anastasis Georgoulas; Jane Hillston; Dimitrios Milios; Guido Sanguinetti (QEST, 2014)

Efficient Stochastic Simulation of Systems with Multiple Time Scales via Statistical Abstraction. - Luca Bortolussi; Dimitrios Milios; Guido Sanguinetti (CMSB, 2015)

Studying Emergent Behaviours in Morphogenesis Using Signal Spatio-Temporal Logic. - Ezio Bartocci; Luca Bortolussi; Dimitrios Milios; Laura Nenzi; Guido Sanguinetti (HSB, 2015)

U-Check: Model Checking and Parameter Synthesis Under Uncertainty. - Luca Bortolussi; Dimitrios Milios; Guido Sanguinetti (QEST, 2015)

Machine Learning Methods in Statistical Model Checking and System Design - Tutorial. - Luca Bortolussi; Dimitrios Milios; Guido Sanguinetti (RV, 2015)

Component aggregation for PEPA models: An approach based on approximate strong equivalence. - Dimitrios Milios; Stephen Gilmore (Perform. Evaluation, 2015)

Policy Learning for Time-Bounded Reachability in Continuous-Time Markov Decision Processes via Doubly-Stochastic Gradient Ascent. - Ezio Bartocci; Luca Bortolussi; Tomás Brázdil; Dimitrios Milios; Guido Sanguinetti (QEST, 2016)

Property-Driven State-Space Coarsening for Continuous Time Markov Chains. - Michalis Michaelides; Dimitrios Milios; Jane Hillston; Guido Sanguinetti (QEST, 2016)

Smoothed model checking for uncertain Continuous-Time Markov Chains. - Luca Bortolussi; Dimitrios Milios; Guido Sanguinetti (Inf. Comput., 2016)

Policy learning in continuous-time Markov decision processes using Gaussian Processes. - Ezio Bartocci; Luca Bortolussi; Tomás Brázdil; Dimitrios Milios; Guido Sanguinetti (Perform. Evaluation, 2017)

Data-Driven Resource Shaping for Compute Clusters. - Francesco Pace; Dimitrios Milios; Damiano Carra; Daniele Venzano; Pietro Michiardi (SoCC, 2018)

Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification. - Dimitrios Milios; Raffaello Camoriano; Pietro Michiardi; Lorenzo Rosasco; Maurizio Filippone (NeurIPS, 2018)

Probabilistic Model Checking for Continuous-Time Markov Chains via Sequential Bayesian Inference. - Dimitrios Milios; Guido Sanguinetti; David Schnoerr (QEST, 2018)

A Data-Driven Approach to Dynamically Adjust Resource Allocation for Compute Clusters. - Francesco Pace; Dimitrios Milios; Damiano Carra; Daniele Venzano; Pietro Michiardi (CoRR, 2018)

Dynamic Resource Shaping for Compute Clusters. - Francesco Pace; Dimitrios Milios; Damiano Carra; Pietro Michiardi (BigData Congress, 2019)

A Variational View on Bootstrap Ensembles as Bayesian Inference. - Dimitrios Milios; Pietro Michiardi; Maurizio Filippone (CoRR, 2020)

Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling. - Giulio Franzese; Rosa Candela; Dimitrios Milios; Maurizio Filippone; Pietro Michiardi (CoRR, 2020)

Sparse within Sparse Gaussian Processes using Neighbor Information. - Gia-Lac Tran; Dimitrios Milios; Pietro Michiardi; Maurizio Filippone (ICML, 2021)

Model Selection for Bayesian Autoencoders. - Ba-Hien Tran; Simone Rossi 001; Dimitrios Milios; Pietro Michiardi; Edwin V. Bonilla; Maurizio Filippone (NeurIPS, 2021)

A Unified View of Stochastic Hamiltonian Sampling. - Giulio Franzese; Dimitrios Milios; Maurizio Filippone; Pietro Michiardi (CoRR, 2021)

A Scalable Bayesian Sampling Method Based on Stochastic Gradient Descent Isotropization. - Giulio Franzese; Dimitrios Milios; Maurizio Filippone; Pietro Michiardi (Entropy, 2021)

Revisiting the Effects of Stochasticity for Hamiltonian Samplers. - Giulio Franzese; Dimitrios Milios; Maurizio Filippone; Pietro Michiardi (ICML, 2022)

Variational Bootstrap for Classification. - Bogdan L. Kozyrskiy; Dimitrios Milios; Maurizio Filippone (KES, 2022)

All You Need is a Good Functional Prior for Bayesian Deep Learning. - Ba-Hien Tran; Simone Rossi 001; Dimitrios Milios; Maurizio Filippone (J. Mach. Learn. Res., 2022)

Imposing Functional Priors on Bayesian Neural Networks. - Bogdan L. Kozyrskiy; Dimitrios Milios; Maurizio Filippone (ICPRAM, 2023)