Hyperbolic Geometry in Computer Vision: A Novel Framework for Convolutional Neural Networks. - Ahmad Bdeir; Kristian Schwethelm; Niels Landwehr (CoRR, 2023)

Fully Hyperbolic Convolutional Neural Networks for Computer Vision. - Ahmad Bdeir; Kristian Schwethelm; Niels Landwehr (ICLR, 2024)

Bounding Reconstruction Attack Success of Adversaries Without Data Priors. - Alexander Ziller; Anneliese Riess; Kristian Schwethelm; Tamara T. Mueller; Daniel Rueckert; Georgios Kaissis (CoRR, 2024)

Visual Privacy Auditing with Diffusion Models. - Kristian Schwethelm; Johannes Kaiser; Moritz Knolle; Daniel Rueckert; Georgios Kaissis; Alexander Ziller (CoRR, 2024)

Laplace Sample Information: Data Informativeness Through a Bayesian Lens. - Johannes Kaiser; Kristian Schwethelm; Daniel Rueckert; Georgios Kaissis (ICLR, 2025)

Differentially Private Active Learning: Balancing Effective Data Selection and Privacy. - Kristian Schwethelm; Johannes Kaiser; Jonas Kuntzer; Mehmet Yigitsoy; Daniel Rückert; Georgios Kaissis (SaTML, 2025)

Visual Privacy Auditing with Diffusion Models. - Kristian Schwethelm; Johannes Kaiser; Moritz Knolle; Sarah Lockfisch; Daniel Rueckert; Alexander Ziller (Trans. Mach. Learn. Res., 2025)

On Arbitrary Predictions from Equally Valid Models. - Sarah Lockfisch; Kristian Schwethelm; Martin J. Menten; Rickmer Braren; Daniel Rueckert; Alexander Ziller; Georgios Kaissis (CoRR, 2025)