4 citations · 10 across the 12 of their papers we have counts for
13 papers
The Misery of Mechanistic Interpretability: A Formal Perspective
Tobias Ladner, Matthias Althoff
Mechanistic interpretability has become the dominant lens for understanding frontier language models, as their inner workings are complex and inherently black boxes. To gain insigh…
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…
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…
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…
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…
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…
Abstraction-Based Proof Production in Formal Verification of Neural Networks
Yizhak Yisrael Elboher, Omri Isac, Guy Katz +2
Modern verification tools for deep neural networks (DNNs) increasingly rely on abstraction to scale to realistic architectures. In parallel, proof production is becoming a critical…
Explaining, Fast and Slow: Abstraction and Refinement of Provable Explanations
Shahaf Bassan, Yizhak Yisrael Elboher, Tobias Ladner +2
Despite significant advancements in post-hoc explainability techniques for neural networks, many current methods rely on heuristics and do not provide formally provable guarantees…
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…
Language Models That Walk the Talk: A Framework for Formal Fairness Certificates
Danqing Chen, Tobias Ladner, Ahmed Rayen Mhadhbi +1
As large language models become integral to high-stakes applications, ensuring their robustness and fairness is critical. Despite their success, large language models remain vulner…
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…
Formal Verification of Graph Convolutional Networks with Uncertain Node Features and Uncertain Graph Structure
Tobias Ladner, Michael Eichelbeck, Matthias Althoff
Graph neural networks are becoming increasingly popular in the field of machine learning due to their unique ability to process data structured in graphs. They have also been appli…
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…