4 papers
DP-MicroAdam: Private and Frugal Algorithm for Training and Fine-tuning
Mihaela Hudişteanu, Nikita P. Kalinin, Edwige Cyffers
Adaptive optimizers are the de facto standard in non-private training as they often enable faster convergence and improved performance. In contrast, differentially private (DP) tra…
Setting is not the Issue in Differential Privacy
Edwige Cyffers
This position paper argues that setting the privacy budget in differential privacy should not be viewed as an important limitation of differential privacy compared to alternative m…
Fedivertex: a Graph Dataset based on Decentralized Social Networks for Trustworthy Machine Learning
Marc Damie, Edwige Cyffers
Decentralized machine learning - where each client keeps its own data locally and uses its own computational resources to collaboratively train a model by exchanging peer-to-peer m…
Optimal Classification under Performative Distribution Shift
Edwige Cyffers, Muni Sreenivas Pydi, Jamal Atif +1
Performative learning addresses the increasingly pervasive situations in which algorithmic decisions may induce changes in the data distribution as a consequence of their public de…