27 citations · 152 across the 28 of their papers we have counts for
7 papers · 1 filter
Synergistic Offline-Online Control Synthesis via Local Gaussian Process Regression
John Jackson, Luca Laurenti, Eric Frew +1
Autonomous systems often have complex and possibly unknown dynamics due to, e.g., black-box components. This leads to unpredictable behaviors and makes control design with performa…
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…
A Language for Modeling And Optimizing Experimental Biological Protocols
Luca Cardelli, Marta Kwiatkowska, Luca Laurenti
Automation is becoming ubiquitous in all laboratory activities, leading towards precisely defined and codified laboratory protocols. However, the integration between laboratory pro…
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…
Strategy Synthesis for Partially-known Switched Stochastic Systems
John Jackson, Luca Laurenti, Eric Frew +1
We present a data-driven framework for strategy synthesis for partially-known switched stochastic systems. The properties of the system are specified using linear temporal logic (L…
Abstracting the Sampling Behaviour of Stochastic Linear Periodic Event-Triggered Control Systems
Giannis Delimpaltadakis, Luca Laurenti, Manuel Mazo
Recently, there have been efforts towards understanding the sampling behaviour of event-triggered control (ETC), for obtaining metrics on its sampling performance and predicting it…
Bayesian Inference with Certifiable Adversarial Robustness
Matthew Wicker, Luca Laurenti, Andrea Patane +3
We consider adversarial training of deep neural networks through the lens of Bayesian learning, and present a principled framework for adversarial training of Bayesian Neural Netwo…