FRAPPÉ: A Group Fairness Framework for Post-Processing Everything

1 code implementation • 5 Dec 2023 • Alexandru Ţifrea, Preethi Lahoti, Ben Packer, Yoni Halpern, Ahmad Beirami, Flavien Prost

Despite achieving promising fairness-error trade-offs, in-processing mitigation techniques for group fairness cannot be employed in numerous practical applications with limited computation resources or no access to the training pipeline of the prediction model.

Fairness

Can semi-supervised learning use all the data effectively? A lower bound perspective

no code implementations • NeurIPS 2023 • Alexandru Ţifrea, Gizem Yüce, Amartya Sanyal, Fanny Yang

Prior works have shown that semi-supervised learning algorithms can leverage unlabeled data to improve over the labeled sample complexity of supervised learning (SL) algorithms.

Interpolation can hurt robust generalization even when there is no noise

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.

regression

Semi-supervised novelty detection using ensembles with regularized disagreement

1 code implementation • 10 Dec 2020 • Alexandru Ţifrea, Eric Stavarache, Fanny Yang

Deep neural networks often predict samples with high confidence even when they come from unseen classes and should instead be flagged for expert evaluation.

Novelty Detection Out-of-Distribution Detection +1