Moritz Knolle

According to our database, Moritz Knolle authored at least 18 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Sensitivity, Specificity, and Consistency: A Tripartite Evaluation of Privacy Filters for Synthetic Data Generation.

Adil Koeken, Alexander Ziller, Moritz Knolle, Daniel Rueckert

CoRR, October, 2025

Visual Privacy Auditing with Diffusion Models.

Kristian Schwethelm, Johannes Kaiser, Moritz Knolle, Sarah Lockfisch, Daniel Rueckert, Alexander Ziller

Trans. Mach. Learn. Res., 2025

Memorisation Bias: AI Predictions for Data Contributors Are Biased Towards Their Health States in the Training Data.

Moritz Knolle, Martin J. Menten, Daniel Rueckert, Georgios Kaissis, Ben Glocker

Proceedings of the Learning with Longitudinal Medical Images and Data, 2025

2024

Memorisation in Machine Learning: A Survey of Results.

Dmitrii Usynin, Moritz Knolle, Georgios Kaissis

Trans. Mach. Learn. Res., 2024

Visual Privacy Auditing with Diffusion Models.

Kristian Schwethelm, Johannes Kaiser, Moritz Knolle, Daniel Rueckert, Georgios Kaissis, Alexander Ziller

CoRR, 2024

2023

SoK: Memorisation in machine learning.

Dmitrii Usynin, Moritz Knolle, Georgios Kaissis

CoRR, 2023

(Predictable) Performance Bias in Unsupervised Anomaly Detection.

Felix Meissen, Svenja Breuer, Moritz Knolle, Alena Buyx, Ruth Müller, Georgios Kaissis, Benedikt Wiestler, Daniel Rueckert

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

Diagnosis.

Daniel Rueckert, Moritz Knolle, Nicolas Duchateau, Reza Razavi, Georgios Kaissis

Proceedings of the AI and Big Data in Cardiology: A Practical Guide, 2023

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

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

2021

Complex-valued deep learning with differential privacy.

Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Kerstin Hammernik, 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

An automatic differentiation system for the age of differential privacy.

Dmitrii Usynin, Alexander Ziller, Moritz Knolle, 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

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 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

2020

Efficient, high-performance pancreatic segmentation using multi-scale feature extraction.

Moritz Knolle, Georgios Kaissis, Friederike Jungmann, Sebastian Ziegelmayer, Daniel Sasse, Marcus R. Makowski, Daniel Rueckert, Rickmer Braren

CoRR, 2020