4 citations · 10 across the 12 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…
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…