1 citations · 1 across the 5 of their papers we have counts for
5 papers
Computing Actual Causes for Neural Network Predictions under Structured Causal Inputs
Jannick Strobel, Muqsit Azeem, Stefan Leue
Explaining the predictions of neural networks is a central challenge in trustworthy AI. Existing explanation methods, such as those based on feature attribution or minimal sufficie…
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…