17 citations · 20 across the 9 of their papers we have counts for
10 papers
The Oracle's Gambit: A Game-Theoretic Framework for Responsible AI Release
Christoph R. Landolt, Tobias Lorenz, Marta Kwiatkowska +1
Responsible vulnerability disclosure can secure the defender's head start by controlling when a vulnerability becomes public. However, this status quo is now challenged by increase…
Scalable Delphi: Large Language Models for Structured Risk Estimation
Tobias Lorenz, Mario Fritz
Quantitative risk assessment relies on structured expert elicitation to estimate unobservable properties. The Delphi method produces calibrated, auditable estimates but requires mo…
Certified Circuits: Stability Guarantees for Mechanistic Circuits
Alaa Anani, Tobias Lorenz, Bernt Schiele +2
Understanding how neural networks arrive at their predictions is essential for debugging, auditing, and deployment. Mechanistic interpretability pursues this goal by identifying ci…
Pixel-level Certified Explanations via Randomized Smoothing
Alaa Anani, Tobias Lorenz, Mario Fritz +1
Post-hoc attribution methods aim to explain deep learning predictions by highlighting influential input pixels. However, these explanations are highly non-robust: small, impercepti…
MIBP-Cert: Certified Training against Data Perturbations with Mixed-Integer Bilinear Programs
Tobias Lorenz, Marta Kwiatkowska, Mario Fritz
Data errors, corruptions, and poisoning attacks during training pose a major threat to the reliability of modern AI systems. While extensive effort has gone into empirical mitigati…
FullCert: Deterministic End-to-End Certification for Training and Inference of Neural Networks
Tobias Lorenz, Marta Kwiatkowska, Mario Fritz
Modern machine learning models are sensitive to the manipulation of both the training data (poisoning attacks) and inference data (adversarial examples). Recognizing this issue, th…
Adaptive Hierarchical Certification for Segmentation using Randomized Smoothing
Alaa Anani, Tobias Lorenz, Bernt Schiele +1
Certification for machine learning is proving that no adversarial sample can evade a model within a range under certain conditions, a necessity for safety-critical domains. Common…
Certifiers Make Neural Networks Vulnerable to Availability Attacks
Tobias Lorenz, Marta Kwiatkowska, Mario Fritz
To achieve reliable, robust, and safe AI systems, it is vital to implement fallback strategies when AI predictions cannot be trusted. Certifiers for neural networks are a reliable…
Robustness Certification for Point Cloud Models
Tobias Lorenz, Anian Ruoss, Mislav Balunović +2
The use of deep 3D point cloud models in safety-critical applications, such as autonomous driving, dictates the need to certify the robustness of these models to real-world transfo…
Feature Generating Networks for Zero-Shot Learning
Yongqin Xian, Tobias Lorenz, Bernt Schiele +1
Suffering from the extreme training data imbalance between seen and unseen classes, most of existing state-of-the-art approaches fail to achieve satisfactory results for the challe…