16 citations · 42 across the 15 of their papers we have counts for
6 papers · 1 filter
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…
Certification of Iterative Predictions in Bayesian Neural Networks
Matthew Wicker, Luca Laurenti, Andrea Patane +3
We consider the problem of computing reach-avoid probabilities for iterative predictions made with Bayesian neural network (BNN) models. Specifically, we leverage bound propagation…
MPC-guided Imitation Learning of Neural Network Policies for the Artificial Pancreas
Hongkai Chen, Nicola Paoletti, Scott A. Smolka +1
Even though model predictive control (MPC) is currently the main algorithm for insulin control in the artificial pancreas (AP), it usually requires complex online optimizations, wh…
Statistical Guarantees for the Robustness of Bayesian Neural Networks
Luca Cardelli, Marta Kwiatkowska, Luca Laurenti +3
We introduce a probabilistic robustness measure for Bayesian Neural Networks (BNNs), defined as the probability that, given a test point, there exists a point within a bounded set…
Neural State Classification for Hybrid Systems
Dung Phan, Nicola Paoletti, Timothy Zhang +3
We introduce the State Classification Problem (SCP) for hybrid systems, and present Neural State Classification (NSC) as an efficient solution technique. SCP generalizes the model…
How to Learn a Model Checker
Dung Phan, Radu Grosu, Nicola Paoletti +2
We show how machine-learning techniques, particularly neural networks, offer a very effective and highly efficient solution to the approximate model-checking problem for continuous…