6 citations · 7 across the 7 of their papers we have counts for
7 papers
Verified SHAP: Provable Bounds for Exact Shapley Values of Neural Networks
David Boetius, Shahaf Bassan, Guy Katz +2
Shapley additive explanations (SHAP) are widely recognised as computationally intractable for neural networks, since they induce an exponential search space over the input features…
Stable Robot Motions on Manifolds: Learning Lyapunov-Constrained Neural Manifold ODEs
David Boetius, Abdelrahman Abdelnaby, Ashok Kumar +3
Learning stable dynamical systems from data is crucial for safe and reliable robot motion planning and control. However, extending stability guarantees to trajectories defined on R…
Counterexample-Guided Repair of Reinforcement Learning Systems Using Safety Critics
David Boetius, Stefan Leue
Naively trained Deep Reinforcement Learning agents may fail to satisfy vital safety constraints. To avoid costly retraining, we may desire to repair a previously trained reinforcem…
Solving Probabilistic Verification Problems of Neural Networks using Branch and Bound
David Boetius, Stefan Leue, Tobias Sutter
Probabilistic verification problems of neural networks are concerned with formally analysing the output distribution of a neural network under a probability distribution of the inp…
Verifying Global Neural Network Specifications using Hyperproperties
David Boetius, Stefan Leue
Current approaches to neural network verification focus on specifications that target small regions around known input data points, such as local robustness. Thus, using these appr…
A Robust Optimisation Perspective on Counterexample-Guided Repair of Neural Networks
David Boetius, Stefan Leue, Tobias Sutter
Counterexample-guided repair aims at creating neural networks with mathematical safety guarantees, facilitating the application of neural networks in safety-critical domains. Howev…
SpecRepair: Counter-Example Guided Safety Repair of Deep Neural Networks
Fabian Bauer-Marquart, David Boetius, Stefan Leue +1
Deep neural networks (DNNs) are increasingly applied in safety-critical domains, such as self-driving cars, unmanned aircraft, and medical diagnosis. It is of fundamental importanc…