32 citations · 43 across the 20 of their papers we have counts for
6 papers · 1 filter
Risk-Averse Certification of Bayesian Neural Networks
Xiyue Zhang, Zifan Wang, Yulong Gao +3
In light of the inherently complex and dynamic nature of real-world environments, incorporating risk measures is crucial for the robustness evaluation of deep learning models. In t…
A data-driven approach for safety quantification of non-linear stochastic systems with unknown additive noise distribution
Frederik Baymler Mathiesen, Licio Romao, Simeon C. Calvert +2
In this paper, we present a novel data-driven approach to quantify safety for non-linear, discrete-time stochastic systems with unknown noise distribution. We define safety as the…
Data-driven memory-dependent abstractions of dynamical systems via a Cantor-Kantorovich metric
Adrien Banse, Licio Romao, Alessandro Abate +1
Abstractions of dynamical systems enable their verification and the design of feedback controllers using simpler, usually discrete, models. In this paper, we propose a data-driven…
Data-driven Interval MDP for Robust Control Synthesis
Rudi Coppola, Andrea Peruffo, Licio Romao +2
The abstraction of dynamical systems is a powerful tool that enables the design of feedback controllers using a correct-by-design framework. We investigate a novel scheme to obtain…
A Stability-Based Abstraction Framework for Reach-Avoid Control of Stochastic Dynamical Systems with Unknown Noise Distributions
Thom Badings, Licio Romao, Alessandro Abate +1
Finite-state abstractions are widely studied for the automated synthesis of correct-by-construction controllers for stochastic dynamical systems. However, existing abstraction meth…
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of Multipliers
Alexandros E. Tzikas, Licio Romao, Mert Pilanci +2
Many machine learning applications require operating on a spatially distributed dataset. Despite technological advances, privacy considerations and communication constraints may pr…