1 code implementation • 26 Apr 2024 • Michael Aerni, Jie Zhang, Florian Tramèr
Empirical defenses for machine learning privacy forgo the provable guarantees of differential privacy in the hope of achieving higher utility while resisting realistic adversaries.
1 code implementation • 18 Jan 2023 • Michael Aerni, Marco Milanta, Konstantin Donhauser, Fanny Yang
Classical wisdom suggests that estimators should avoid fitting noise to achieve good generalization.
2 code implementations • NeurIPS 2021 • Konstantin Donhauser, Alexandru Ţifrea, Michael Aerni, Reinhard Heckel, Fanny Yang
Numerous recent works show that overparameterization implicitly reduces variance for min-norm interpolators and max-margin classifiers.
1 code implementation • ICML Workshop AML 2021 • Konstantin Donhauser, Alexandru Tifrea, Michael Aerni, Reinhard Heckel, Fanny Yang
Numerous recent works show that overparameterization implicitly reduces variance, suggesting vanishing benefits for explicit regularization in high dimensions.