4 citations · 4 across the 1 of their papers we have counts for
6 papers
Adversarial Robustness Guarantees for Gaussian Processes
Andrea Patane, Arno Blaas, Luca Laurenti +3
Gaussian processes (GPs) enable principled computation of model uncertainty, making them attractive for safety-critical applications. Such scenarios demand that GP decisions are no…
Probabilistic Safety for Bayesian Neural Networks
Matthew Wicker, Luca Laurenti, Andrea Patane +1
We study probabilistic safety for Bayesian Neural Networks (BNNs) under adversarial input perturbations. Given a compact set of input points, , we study t…
Robustness of Bayesian Neural Networks to Gradient-Based Attacks
Ginevra Carbone, Matthew Wicker, Luca Laurenti +3
Vulnerability to adversarial attacks is one of the principal hurdles to the adoption of deep learning in safety-critical applications. Despite significant efforts, both practical a…
Safety Guarantees for Planning Based on Iterative Gaussian Processes
Kyriakos Polymenakos, Luca Laurenti, Andrea Patane +5
Gaussian Processes (GPs) are widely employed in control and learning because of their principled treatment of uncertainty. However, tracking uncertainty for iterative, multi-step p…
Adversarial Robustness Guarantees for Classification with Gaussian Processes
Arno Blaas, Andrea Patane, Luca Laurenti +3
We investigate adversarial robustness of Gaussian Process Classification (GPC) models. Given a compact subset of the input space enclosing a test point $x…
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…