Learning games for configuration and diagnosis tasks. - Alexander Felfernig; Michael Jeran; Thorsten Ruprechter; Alexander Ziller; Stefan Reiterer; Martin Stettinger (Configuration Workshop, 2015)

Oktoberfest Food Dataset. - Alexander Ziller; Julius Hansjakob; Vitalii Rusinov; Daniel Zügner; Peter Vogel; Stephan Günnemann (CoRR, 2019)

Privacy-preserving medical image analysis. - Alexander Ziller; Jonathan Passerat-Palmbach; Théo Ryffel; Dmitrii Usynin; Andrew Trask; Ionésio Da Lima Costa Junior; Jason Mancuso; Marcus R. Makowski; Daniel Rueckert; Rickmer Braren; Georgios Kaissis (CoRR, 2020)

Differentially private federated deep learning for multi-site medical image segmentation. - Alexander Ziller; Dmitrii Usynin; Nicolas Remerscheid; Moritz Knolle; Marcus R. Makowski; Rickmer Braren; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation. - Alexander Ziller; Dmitrii Usynin; Moritz Knolle; Kritika Prakash; Andrew Trask; Rickmer Braren; Marcus R. Makowski; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Differentially private training of neural networks with Langevin dynamics forcalibrated predictive uncertainty. - Moritz Knolle; Alexander Ziller; Dmitrii Usynin; Rickmer Braren; Marcus R. Makowski; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

An automatic differentiation system for the age of differential privacy. - Dmitrii Usynin; Alexander Ziller; Moritz Knolle; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Partial sensitivity analysis in differential privacy. - Tamara T. Mueller; Alexander Ziller; Dmitrii Usynin; Moritz Knolle; Friederike Jungmann; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Complex-valued deep learning with differential privacy. - Alexander Ziller; Dmitrii Usynin; Moritz Knolle; Kerstin Hammernik; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Distributed Machine Learning and the Semblance of Trust. - Dmitrii Usynin; Alexander Ziller; Daniel Rueckert; Jonathan Passerat-Palmbach; Georgios Kaissis (CoRR, 2021)

End-to-end privacy preserving deep learning on multi-institutional medical imaging. - Georgios Kaissis; Alexander Ziller; Jonathan Passerat-Palmbach; Théo Ryffel; Dmitrii Usynin; Andrew Trask; Ionésio Lima; Jason Mancuso; Friederike Jungmann; Marc-Matthias Steinborn; Andreas Saleh; Marcus R. Makowski; et al. (Nat. Mach. Intell., 2021)

Adversarial interference and its mitigations in privacy-preserving collaborative machine learning. - Dmitrii Usynin; Alexander Ziller; Marcus R. Makowski; Rickmer Braren; Daniel Rueckert; Ben Glocker; Georgios Kaissis; Jonathan Passerat-Palmbach (Nat. Mach. Intell., 2021)

Differentially private training of residual networks with scale normalisation. - Helena Klause; Alexander Ziller; Daniel Rueckert; Kerstin Hammernik; Georgios Kaissis (CoRR, 2022)

SmoothNets: Optimizing CNN architecture design for differentially private deep learning. - Nicolas W. Remerscheid; Alexander Ziller; Daniel Rueckert; Georgios Kaissis (CoRR, 2022)

Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens. - Georgios Kaissis; Alexander Ziller; Stefan Kolek Martinez de Azagra; Daniel Rueckert (CoRR, 2022)

How Do Input Attributes Impact the Privacy Loss in Differential Privacy? - Tamara T. Mueller; Stefan Kolek; Friederike Jungmann; Alexander Ziller; Dmitrii Usynin; Moritz Knolle; Daniel Rueckert; Georgios Kaissis (CoRR, 2022)

Privacy: An Axiomatic Approach. - Alexander Ziller; Tamara T. Mueller; Rickmer Braren; Daniel Rueckert; Georgios Kaissis (Entropy, 2022)

Unified Interpretation of the Gaussian Mechanism for Differential Privacy Through the Sensitivity Index. - Georgios Kaissis; Moritz Knolle; Friederike Jungmann; Alexander Ziller; Dmitrii Usynin; Daniel Rueckert (J. Priv. Confidentiality, 2022)

Exploiting Segmentation Labels and Representation Learning to Forecast Therapy Response of PDAC Patients. - Alexander Ziller; Ayhan Can Erdur; Friederike Jungmann; Daniel Rueckert; Rickmer Braren; Georgios Kaissis (ISBI, 2023)

Body Fat Estimation from Surface Meshes Using Graph Neural Networks. - Tamara T. Mueller; Siyu Zhou; Sophie Starck; Friederike Jungmann; Alexander Ziller; Orhun Aksoy; Danylo Movchan; Rickmer Braren; Georgios Kaissis; Daniel Rueckert (ShapeMI@MICCAI, 2023)

Optimal privacy guarantees for a relaxed threat model: Addressing sub-optimal adversaries in differentially private machine learning. - Georgios Kaissis; Alexander Ziller; Stefan Kolek; Anneliese Riess; Daniel Rueckert (NeurIPS, 2023)

Private, fair and accurate: Training large-scale, privacy-preserving AI models in radiology. - Soroosh Tayebi Arasteh; Alexander Ziller; Christiane Kuhl; Marcus R. Makowski; Sven Nebelung; Rickmer Braren; Daniel Rueckert; Daniel Truhn; Georgios Kaissis (CoRR, 2023)

Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy. - Georgios Kaissis; Jamie Hayes; Alexander Ziller; Daniel Rueckert (CoRR, 2023)

Explainable 2D Vision Models for 3D Medical Data. - Alexander Ziller; Alp Güvenir; Ayhan Can Erdur; Tamara T. Mueller; Philip Müller; Friederike Jungmann; Johannes Brandt; Jan Peeken; Rickmer Braren; Daniel Rueckert; Georgios Kaissis (CoRR, 2023)

Bias-Aware Minimisation: Understanding and Mitigating Estimator Bias in Private SGD. - Moritz Knolle; Robert Dorfman; Alexander Ziller; Daniel Rueckert; Georgios Kaissis (CoRR, 2023)

How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection. - Felix Meissen; Johannes Getzner; Alexander Ziller; Georgios Kaissis; Daniel Rueckert (CoRR, 2023)

Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging. - Alexander Ziller; Tamara T. Mueller; Simon Stieger; Leonhard F. Feiner; Johannes Brandt; Rickmer Braren; Daniel Rueckert; Georgios Kaissis (CoRR, 2023)

Fair and Private CT Contrast Agent Detection. - Philipp Kaess; Alexander Ziller; Lea Mantz; Daniel Rueckert; Florian J. Fintelmann; Georgios Kaissis (FAIMI/EPIMI@MICCAI, 2024)

Complex-Valued Federated Learning with Differential Privacy and MRI Applications. - Anneliese Riess; Alexander Ziller; Stefan Kolek; Daniel Rueckert; Julia A. Schnabel; Georgios Kaissis (ISIC/iMIMIC/EARTH/DeCaF@MICCAI, 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)

Reconciling privacy and accuracy in AI for medical imaging. - Alexander Ziller; Tamara T. Mueller; Simon Stieger; Leonhard F. Feiner; Johannes Brandt; Rickmer Braren; Daniel Rueckert; Georgios Kaissis (Nat. Mac. Intell., 2024)

Are Population Graphs Really as Powerful as Believed? - Tamara T. Müller; Sophie Starck; Kyriaki-Margarita Bintsi; Alexander Ziller; Rickmer Braren; Georgios Kaissis; Daniel Rueckert (Trans. Mach. Learn. Res., 2024)

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)