activity
20092022
most citedMonitoring Mobile and Spatially Distributed Cyber-Physical Systems

79 citations · 114 across the 12 of their papers we have counts for

collaborators

27 papers

cs.AI20221 cited

Graph Neural Networks for Propositional Model Counting

Gaia Saveri, Luca Bortolussi

Graph Neural Networks (GNNs) have been recently leveraged to solve several logical reasoning tasks. Nevertheless, counting problems such as propositional model counting (#SAT) are…

cs.LO2022

Learning Model Checking and the Kernel Trick for Signal Temporal Logic on Stochastic Processes

Luca Bortolussi, Giuseppe Maria Gallo, Jan Křetínský +1

We introduce a similarity function on formulae of signal temporal logic (STL). It comes in the form of a kernel function, well known in machine learning as a conceptually and compu…

stat.ME2021

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…

cs.LG2021

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…

cs.LG2021

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…

stat.ML2021

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…