189 citations · 339 across the 48 of their papers we have counts for
13 papers · 1 filter
Improved Localized Machine Unlearning Through the Lens of Memorization
Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Georgios Kaissis +3
Machine unlearning refers to removing the influence of a specified subset of training data from a machine learning model, efficiently, after it has already been trained. This is im…
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
Kristian Schwethelm, Johannes Kaiser, Jonas Kuntzer +3
Active learning (AL) is a widely used technique for optimizing data labeling in machine learning by iteratively selecting, labeling, and training on the most informative data. Howe…
Estimating Neural Orientation Distribution Fields on High Resolution Diffusion MRI Scans
Mohammed Munzer Dwedari, William Consagra, Philip Müller +3
The Orientation Distribution Function (ODF) characterizes key brain microstructural properties and plays an important role in understanding brain structural connectivity. Recent wo…
Machine Unlearning for Medical Imaging
Reza Nasirigerdeh, Nader Razmi, Julia A. Schnabel +2
Machine unlearning is the process of removing the impact of a particular set of training samples from a pretrained model. It aims to fulfill the "right to be forgotten", which gran…
Evaluation of Language Models in the Medical Context Under Resource-Constrained Settings
Andrea Posada, Daniel Rueckert, Felix Meissen +1
Since the Transformer architecture emerged, language model development has grown, driven by their promising potential. Releasing these models into production requires properly unde…
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy
Georgios Kaissis, Stefan Kolek, Borja Balle +2
In differentially private (DP) machine learning, the privacy guarantees of DP mechanisms are often reported and compared on the basis of a single -pair. This prac…