53 citations · 53 across the 3 of their papers we have counts for
3 papers
cs.LG2025
Efficient Machine Unlearning by Model Splitting and Core Sample Selection
Maximilian Egger, Rawad Bitar, Rüdiger Urbanke
Machine unlearning is essential for meeting legal obligations such as the right to be forgotten, which requires the removal of specific data from machine learning models upon reque…
cs.CR2025
Federated One-Shot Learning with Data Privacy and Objective-Hiding
Maximilian Egger, Rüdiger Urbanke, Rawad Bitar
Privacy in federated learning is crucial, encompassing two key aspects: safeguarding the privacy of clients' data and maintaining the privacy of the federator's objective from the…
cs.IT2014★ 53 cited
A Scaling Law to Predict the Finite-Length Performance of Spatially-Coupled LDPC Codes
Pablo M. Olmos, Rüdiger Urbanke
Spatially-coupled LDPC codes are known to have excellent asymptotic properties. Much less is known regarding their finite-length performance. We propose a scaling law to predict th…