| Smoothed model checking for uncertain continuous-time Markov chains L Bortolussi, D Milios, G Sanguinetti Information and Computation 247, 235-253, 2016 | 116 | 2016 |
| Dirichlet-based gaussian processes for large-scale calibrated classification D Milios, R Camoriano, P Michiardi, L Rosasco, M Filippone Advances in Neural Information Processing Systems 31, 2018 | 94 | 2018 |
| All you need is a good functional prior for Bayesian deep learning BH Tran, S Rossi, D Milios, M Filippone Journal of Machine Learning Research 23 (74), 1-56, 2022 | 74 | 2022 |
| U-check: Model checking and parameter synthesis under uncertainty L Bortolussi, D Milios, G Sanguinetti International Conference on Quantitative Evaluation of Systems, 89-104, 2015 | 39 | 2015 |
| Studying emergent behaviours in morphogenesis using signal spatio-temporal logic E Bartocci, L Bortolussi, D Milios, L Nenzi, G Sanguinetti Hybrid Systems Biology: Fourth International Workshop, HSB 2015, Madrid …, 2015 | 24 | 2015 |
| Probabilistic programming process algebra A Georgoulas, J Hillston, D Milios, G Sanguinetti Quantitative Evaluation of Systems: 11th International Conference, QEST 2014 …, 2014 | 21 | 2014 |
| Global optimization of analogy-based software cost estimation with genetic algorithms D Milios, I Stamelos, C Chatzibagias International Conference on Engineering Applications of Neural Networks, 350-359, 2011 | 19 | 2011 |
| Sparse within sparse gaussian processes using neighbor information GL Tran, D Milios, P Michiardi, M Filippone International Conference on Machine Learning, 10369-10378, 2021 | 18 | 2021 |
| Efficient stochastic simulation of systems with multiple time scales via statistical abstraction L Bortolussi, D Milios, G Sanguinetti International Conference on Computational Methods in Systems Biology, 40-51, 2015 | 14 | 2015 |
| Model selection for bayesian autoencoders BH Tran, S Rossi, D Milios, P Michiardi, EV Bonilla, M Filippone Advances in Neural Information Processing Systems 34, 19730-19742, 2021 | 13 | 2021 |
| Probability distributions as program variables D Milios Edinburgh, UK: School of Informatics, University of Edinburgh, MS Thesis, 2009 | 11 | 2009 |
| Markov chain simulation with fewer random samples D Milios, S Gilmore Electronic Notes in Theoretical Computer Science 296, 183-197, 2013 | 10 | 2013 |
| Probabilistic model checking for continuous-time Markov chains via sequential Bayesian inference D Milios, G Sanguinetti, D Schnoerr Quantitative Evaluation of Systems: 15th International Conference, QEST 2018 …, 2018 | 9 | 2018 |
| Policy learning in continuous-time markov decision processes using gaussian processes E Bartocci, L Bortolussi, T Brázdil, D Milios, G Sanguinetti Performance Evaluation 116, 84-100, 2017 | 8 | 2017 |
| Policy learning for time-bounded reachability in continuous-time Markov decision processes via doubly-stochastic gradient ascent E Bartocci, L Bortolussi, T Brázdil, D Milios, G Sanguinetti Quantitative Evaluation of Systems: 13th International Conference, QEST 2016 …, 2016 | 8 | 2016 |
| Component aggregation for PEPA models: An approach based on approximate strong equivalence D Milios, S Gilmore Performance Evaluation 94, 43-71, 2015 | 8 | 2015 |
| A scalable bayesian sampling method based on stochastic gradient descent isotropization G Franzese, D Milios, M Filippone, P Michiardi Entropy 23 (11), 1426, 2021 | 7 | 2021 |
| Machine learning methods in statistical model checking and system design–tutorial L Bortolussi, D Milios, G Sanguinetti Runtime Verification: 6th International Conference, RV 2015, Vienna, Austria …, 2015 | 7 | 2015 |
| A genetic algorithm approach to global optimization of software cost estimation by analogy D Milios, I Stamelos, C Chatzibagias Intelligent Decision Technologies 7 (1), 45-58, 2013 | 7 | 2013 |
| Probabilistic model checking for continuous time markov chains via sequential bayesian inference D Milios, G Sanguinetti, D Schnoerr arXiv preprint arXiv:1711.01863, 2017 | 5 | 2017 |
