116 citations · 183 across the 17 of their papers we have counts for
20 papers
Certification of Distributional Individual Fairness
Matthew Wicker, Vihari Piratia, Adrian Weller
Providing formal guarantees of algorithmic fairness is of paramount importance to socially responsible deployment of machine learning algorithms. In this work, we study formal guar…
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}…
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…
Use Perturbations when Learning from Explanations
Juyeon Heo, Vihari Piratla, Matthew Wicker +1
Machine learning from explanations (MLX) is an approach to learning that uses human-provided explanations of relevant or irrelevant features for each input to ensure that model pre…
Robust Explanation Constraints for Neural Networks
Matthew Wicker, Juyeon Heo, Luca Costabello +1
Post-hoc explanation methods are used with the intent of providing insights about neural networks and are sometimes said to help engender trust in their outputs. However, popular e…