79 citations · 424 across the 54 of their papers we have counts for
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Variance Reduction in Stochastic Reaction Networks using Control Variates
Michael Backenköhler, Luca Bortolussi, Verena Wolf
Monte Carlo estimation in plays a crucial role in stochastic reaction networks. However, reducing the statistical uncertainty of the corresponding estimators requires sampling a la…
Neural Predictive Monitoring under Partial Observability
Francesca Cairoli, Luca Bortolussi, Nicola Paoletti
We consider the problem of predictive monitoring (PM), i.e., predicting at runtime future violations of a system from the current state. We work under the most realistic settings w…
Abstraction of Markov Population Dynamics via Generative Adversarial Nets
Francesca Cairoli, Ginevra Carbone, Luca Bortolussi
Markov Population Models are a widespread formalism used to model the dynamics of complex systems, with applications in Systems Biology and many other fields. The associated Markov…
Abstraction-Guided Truncations for Stationary Distributions of Markov Population Models
Michael Backenköhler, Luca Bortolussi, Gerrit Großmann +1
To understand the long-run behavior of Markov population models, the computation of the stationary distribution is often a crucial part. We propose a truncation-based approximation…
MoonLight: A Lightweight Tool for Monitoring Spatio-Temporal Properties
Ezio Bartocci, Luca Bortolussi, Michele Loreti +2
We present MoonLight, a tool for monitoring temporal and spatio-temporal properties of mobile and spatially distributed cyber-physical systems (CPS). In the proposed framework, spa…
Resilience of Bayesian Layer-Wise Explanations under Adversarial Attacks
Ginevra Carbone, Guido Sanguinetti, Luca Bortolussi
We consider the problem of the stability of saliency-based explanations of Neural Network predictions under adversarial attacks in a classification task. Saliency interpretations o…
Random Projections for Improved Adversarial Robustness
Ginevra Carbone, Guido Sanguinetti, Luca Bortolussi
We propose two training techniques for improving the robustness of Neural Networks to adversarial attacks, i.e. manipulations of the inputs that are maliciously crafted to fool net…