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2020 – today
- 2022
[c16]Giulio Franzese, Dimitrios Milios, Maurizio Filippone, Pietro Michiardi:
Revisiting the Effects of Stochasticity for Hamiltonian Samplers. ICML 2022: 6744-6778- 2021
- [j7]Giulio Franzese
, Dimitrios Milios, Maurizio Filippone, Pietro Michiardi:
A Scalable Bayesian Sampling Method Based on Stochastic Gradient Descent Isotropization. Entropy 23(11): 1426 (2021) - [c15]Gia-Lac Tran, Dimitrios Milios, Pietro Michiardi, Maurizio Filippone:
Sparse within Sparse Gaussian Processes using Neighbor Information. ICML 2021: 10369-10378 - [c14]Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone:
Model Selection for Bayesian Autoencoders. NeurIPS 2021: 19730-19742 - [i11]Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone:
Model Selection for Bayesian Autoencoders. CoRR abs/2106.06245 (2021) - [i10]Giulio Franzese, Dimitrios Milios, Maurizio Filippone, Pietro Michiardi:
A Unified View of Stochastic Hamiltonian Sampling. CoRR abs/2106.16200 (2021) - 2020
- [i9]Dimitrios Milios, Pietro Michiardi, Maurizio Filippone:
A Variational View on Bootstrap Ensembles as Bayesian Inference. CoRR abs/2006.04548 (2020) - [i8]Giulio Franzese, Rosa Candela, Dimitrios Milios, Maurizio Filippone, Pietro Michiardi:
Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling. CoRR abs/2006.05087 (2020) - [i7]Gia-Lac Tran, Dimitrios Milios, Pietro Michiardi, Maurizio Filippone:
Sparse within Sparse Gaussian Processes using Neighbor Information. CoRR abs/2011.05041 (2020) - [i6]Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Maurizio Filippone:
All You Need is a Good Functional Prior for Bayesian Deep Learning. CoRR abs/2011.12829 (2020)
2010 – 2019
- 2019
- [c13]Francesco Pace, Dimitrios Milios, Damiano Carra, Pietro Michiardi:
Dynamic Resource Shaping for Compute Clusters. BigData Congress 2019: 45-54 - 2018
- [c12]Francesco Pace, Dimitrios Milios, Damiano Carra, Daniele Venzano, Pietro Michiardi:
Data-Driven Resource Shaping for Compute Clusters. SoCC 2018: 527 - [c11]Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, Maurizio Filippone:
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification. NeurIPS 2018: 6008-6018 - [c10]Dimitrios Milios, Guido Sanguinetti, David Schnoerr:
Probabilistic Model Checking for Continuous-Time Markov Chains via Sequential Bayesian Inference. QEST 2018: 289-305 - [i5]Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, Maurizio Filippone:
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification. CoRR abs/1805.10915 (2018) - [i4]Francesco Pace, Dimitrios Milios, Damiano Carra, Daniele Venzano, Pietro Michiardi:
A Data-Driven Approach to Dynamically Adjust Resource Allocation for Compute Clusters. CoRR abs/1807.00368 (2018) - 2017
- [j6]Ezio Bartocci, Luca Bortolussi, Tomás Brázdil, Dimitrios Milios, Guido Sanguinetti:
Policy learning in continuous-time Markov decision processes using Gaussian Processes. Perform. Evaluation 116: 84-100 (2017) - [i3]Dimitrios Milios, Guido Sanguinetti, David Schnoerr:
Probabilistic Model Checking for Continuous Time Markov Chains via Sequential Bayesian Inference. CoRR abs/1711.01863 (2017) - 2016
- [j5]Luca Bortolussi, Dimitrios Milios, Guido Sanguinetti:
Smoothed model checking for uncertain Continuous-Time Markov Chains. Inf. Comput. 247: 235-253 (2016) - [c9]Michalis Michaelides, Dimitrios Milios, Jane Hillston, Guido Sanguinetti:
Property-Driven State-Space Coarsening for Continuous Time Markov Chains. QEST 2016: 3-18 - [c8]Ezio Bartocci, Luca Bortolussi, Tomás Brázdil, Dimitrios Milios, Guido Sanguinetti:
Policy Learning for Time-Bounded Reachability in Continuous-Time Markov Decision Processes via Doubly-Stochastic Gradient Ascent. QEST 2016: 244-259 - [i2]Ezio Bartocci, Luca Bortolussi, Tomás Brázdil, Dimitrios Milios, Guido Sanguinetti:
Policy learning for time-bounded reachability in Continuous-Time Markov Decision Processes via doubly-stochastic gradient ascent. CoRR abs/1605.09703 (2016) - [i1]Michalis Michaelides, Dimitrios Milios, Jane Hillston, Guido Sanguinetti:
Property-driven State-Space Coarsening for Continuous Time Markov Chains. CoRR abs/1606.01111 (2016) - 2015
- [j4]Dimitrios Milios, Stephen Gilmore:
Component aggregation for PEPA models: An approach based on approximate strong equivalence. Perform. Evaluation 94: 43-71 (2015) - [c7]Luca Bortolussi, Dimitrios Milios, Guido Sanguinetti:
Efficient Stochastic Simulation of Systems with Multiple Time Scales via Statistical Abstraction. CMSB 2015: 40-51 - [c6]Ezio Bartocci, Luca Bortolussi, Dimitrios Milios, Laura Nenzi, Guido Sanguinetti:
Studying Emergent Behaviours in Morphogenesis Using Signal Spatio-Temporal Logic. HSB 2015: 156-172 - [c5]Luca Bortolussi, Dimitrios Milios, Guido Sanguinetti:
U-Check: Model Checking and Parameter Synthesis Under Uncertainty. QEST 2015: 89-104 - [c4]Luca Bortolussi, Dimitrios Milios, Guido Sanguinetti:
Machine Learning Methods in Statistical Model Checking and System Design - Tutorial. RV 2015: 323-341 - 2014
- [c3]Anastasis Georgoulas, Jane Hillston, Dimitrios Milios, Guido Sanguinetti:
Probabilistic Programming Process Algebra. QEST 2014: 249-264 - 2013
- [j3]Chris J. Banks, Allan Clark, Anastasis Georgoulas, Stephen Gilmore, Jane Hillston, Dimitrios Milios, Ian Stark:
Stochastic Modelling of the Kai-based Circadian Clock. Electron. Notes Theor. Comput. Sci. 296: 43-60 (2013) - [j2]Dimitrios Milios, Stephen Gilmore:
Markov Chain Simulation with Fewer Random Samples. Electron. Notes Theor. Comput. Sci. 296: 183-197 (2013) - [j1]Dimitrios Milios, Ioannis Stamelos, Christos Chatzibagias:
A genetic algorithm approach to global optimization of software cost estimation by analogy. Intell. Decis. Technol. 7(1): 45-58 (2013) - 2012
- [c2]Dimitrios Milios, Stephen Gilmore:
Compositional Approximate Markov Chain Aggregation for PEPA Models. EPEW/UKPEW 2012: 96-110 - 2011
- [c1]Dimitrios Milios, Ioannis Stamelos, Christos Chatzibagias:
Global Optimization of Analogy-Based Software Cost Estimation with Genetic Algorithms. EANN/AIAI (2) 2011: 350-359
Coauthor Index
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