no code implementations • 13 Jun 2021 • Luca Cardelli, Marta Kwiatkowska, Luca Laurenti
We should ideally start from an integrated description of both the model and the steps carried out to test it, to concurrently analyze uncertainties in model parameters, equipment tolerances, and data collection.
1 code implementation • 7 Apr 2021 • Andrea Patane, Arno Blaas, Luca Laurenti, Luca Cardelli, Stephen Roberts, Marta Kwiatkowska
Gaussian processes (GPs) enable principled computation of model uncertainty, making them attractive for safety-critical applications.
no code implementations • 9 Jan 2021 • Luca Cardelli, Isabel Cristina Perez-Verona, Mirco Tribastone, Max Tschaikowski, Andrea Vandin, Tabea Waizmann
Motivation: Stochastic reaction networks are a widespread model to describe biological systems where the presence of noise is relevant, such as in cell regulatory processes.
no code implementations • 29 Nov 2019 • Kyriakos Polymenakos, Luca Laurenti, Andrea Patane, Jan-Peter Calliess, Luca Cardelli, Marta Kwiatkowska, Alessandro Abate, Stephen Roberts
Gaussian Processes (GPs) are widely employed in control and learning because of their principled treatment of uncertainty.
no code implementations • 25 Sep 2019 • Luca Laurenti, Andrea Patane, Matthew Wicker, Luca Bortolussi, Luca Cardelli, Marta Kwiatkowska
We investigate global adversarial robustness guarantees for machine learning models.
no code implementations • 21 Sep 2019 • Rhiannon Michelmore, Matthew Wicker, Luca Laurenti, Luca Cardelli, Yarin Gal, Marta Kwiatkowska
Deep neural network controllers for autonomous driving have recently benefited from significant performance improvements, and have begun deployment in the real world.
1 code implementation • 28 May 2019 • Arno Blaas, Andrea Patane, Luca Laurenti, Luca Cardelli, Marta Kwiatkowska, Stephen Roberts
We apply our method to investigate the robustness of GPC models on a 2D synthetic dataset, the SPAM dataset and a subset of the MNIST dataset, providing comparisons of different GPC training techniques, and show how our method can be used for interpretability analysis.
1 code implementation • 5 Mar 2019 • Luca Cardelli, Marta Kwiatkowska, Luca Laurenti, Nicola Paoletti, Andrea Patane, Matthew Wicker
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 such that the BNN prediction differs between the two.
1 code implementation • 17 Sep 2018 • Luca Cardelli, Marta Kwiatkowska, Luca Laurenti, Andrea Patane
Bayesian inference and Gaussian processes are widely used in applications ranging from robotics and control to biological systems.