27 citations · 152 across the 28 of their papers we have counts for
5 papers · 1 filter
Probabilistic Reach-Avoid for Bayesian Neural Networks
Matthew Wicker, Luca Laurenti, Andrea Patane +3
Model-based reinforcement learning seeks to simultaneously learn the dynamics of an unknown stochastic environment and synthesise an optimal policy for acting in it. Ensuring the s…
Adversarial Robustness Certification for Bayesian Neural Networks
Matthew Wicker, Andrea Patane, Luca Laurenti +1
We study the problem of certifying the robustness of Bayesian neural networks (BNNs) to adversarial input perturbations. Given a compact set of input points $T \subseteq \mathbb{R}…
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming
Steven Adams, Andrea Patane, Morteza Lahijanian +1
In this paper, we introduce BNN-DP, an efficient algorithmic framework for analysis of adversarial robustness of Bayesian Neural Networks (BNNs). Given a compact set of input point…
Individual Fairness in Bayesian Neural Networks
Alice Doherty, Matthew Wicker, Luca Laurenti +1
We study Individual Fairness (IF) for Bayesian neural networks (BNNs). Specifically, we consider the --individual fairness notion, which requires that, for any pair of input…
Inner approximations of stochastic programs for data-driven stochastic barrier function design
Frederik Baymler Mathiesen, Licio Romao, Simeon C. Calvert +2
This paper proposes a new framework to compute finite-horizon safety guarantees for discrete-time piece-wise affine systems with stochastic noise of unknown distributions. The appr…