28 citations · 35 across the 4 of their papers we have counts for
6 papers
Fair Normalizing Flows
Mislav Balunović, Anian Ruoss, Martin Vechev
Fair representation learning is an attractive approach that promises fairness of downstream predictors by encoding sensitive data. Unfortunately, recent work has shown that strong…
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…
On the Paradox of Certified Training
Nikola Jovanović, Mislav Balunović, Maximilian Baader +1
Certified defenses based on convex relaxations are an established technique for training provably robust models. The key component is the choice of relaxation, varying from simple…
Efficient Certification of Spatial Robustness
Anian Ruoss, Maximilian Baader, Mislav Balunović +1
Recent work has exposed the vulnerability of computer vision models to vector field attacks. Due to the widespread usage of such models in safety-critical applications, it is cruci…
Scalable Polyhedral Verification of Recurrent Neural Networks
Wonryong Ryou, Jiayu Chen, Mislav Balunovic +3
We present a scalable and precise verifier for recurrent neural networks, called Prover based on two novel ideas: (i) a method to compute a set of polyhedral abstractions for the n…
Learning Certified Individually Fair Representations
Anian Ruoss, Mislav Balunović, Marc Fischer +1
Fair representation learning provides an effective way of enforcing fairness constraints without compromising utility for downstream users. A desirable family of such fairness cons…