2 citations · 2 across the 1 of their papers we have counts for
12 papers
Training Verifiably Robust Agents Using Set-Based Reinforcement Learning
Manuel Wendl, Lukas Koller, Tobias Ladner +1
Reinforcement learning policies parametrized by deep neural networks have achieved strong performance for continuous control, yet even small input perturbations may lead to unpredi…
Perception with Guarantees: Certified Pose Estimation via Reachability Analysis
Tobias Ladner, Yasser Shoukry, Matthias Althoff
Agents in cyber-physical systems are increasingly entrusted with safety-critical tasks. Ensuring safety of these agents often requires localizing the pose for subsequent actions. P…
Formally Verifying Analog Neural Networks Under Process Variations Using Polynomial Zonotopes
Yasmine Abu-Haeyeh, Tobias Ladner, Matthias Althoff +1
Analog neural networks are gaining attention due to their efficiency in terms of power consumption and processing speed. However, since analog neural networks are implemented as ph…
Set-Based Training of Neural Barrier Certificates for Safety Verification of Dynamical Systems
Miriam Kranzlmüller, Lukas Koller, Tobias Ladner +1
Barrier certificates are scalar functions over the state space of dynamical systems that separate all unsafe states from all reachable states. The existence of a barrier certificat…
Provably Explaining Neural Additive Models
Shahaf Bassan, Yizhak Yisrael Elboher, Tobias Ladner +4
Despite significant progress in post-hoc explanation methods for neural networks, many remain heuristic and lack provable guarantees. A key approach for obtaining explanations with…
Out of the Shadows: Exploring a Latent Space for Neural Network Verification
Lukas Koller, Tobias Ladner, Matthias Althoff
Neural networks are ubiquitous. However, they are often sensitive to small input changes. Hence, to prevent unexpected behavior in safety-critical applications, their formal verifi…