5 citations · 9 across the 7 of their papers we have counts for
4 papers · 1 filter
Step-resolved data attribution for looped transformers
Georgios Kaissis, David Mildenberger, Juan Felipe Gomez +2
We study how individual training examples shape the internal computation of looped transformers, where a shared block is applied for recurrent iterations to enable latent reaso…
Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy
Bogdan Kulynych, Juan Felipe Gomez, Georgios Kaissis +4
Differentially private (DP) mechanisms are difficult to interpret and calibrate because existing methods for mapping standard privacy parameters to concrete privacy risks -- re-ide…
Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning
Juan Felipe Gomez, Bogdan Kulynych, Georgios Kaissis +4
Current practices for reporting differential privacy (DP) guarantees for machine learning (ML) algorithms such as DP-SGD provide an incomplete and potentially misleading picture. F…
Attack-Aware Noise Calibration for Differential Privacy
Bogdan Kulynych, Juan Felipe Gomez, Georgios Kaissis +2
Differential privacy (DP) is a widely used approach for mitigating privacy risks when training machine learning models on sensitive data. DP mechanisms add noise during training to…