2 citations · 2 across the 2 of their papers we have counts for
5 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…
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…
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…
The 6th International Verification of Neural Networks Competition (VNN-COMP 2025): Summary and Results
Konstantin Kaulen, Tobias Ladner, Stanley Bak +8
This report summarizes the 6th International Verification of Neural Networks Competition (VNN-COMP 2025), held as a part of the 8th International Symposium on AI Verification (SAIV…
Set-Based Training for Neural Network Verification
Lukas Koller, Tobias Ladner, Matthias Althoff
Neural networks are vulnerable to adversarial attacks, i.e., small input perturbations can significantly affect the outputs of a neural network. Therefore, to ensure safety of neur…