Dataset distillation introduces fairness gaps from subgroup pattern mismatches rather than just imbalance; distilling to a group-agnostic barycenter of predictive information reduces these gaps.
The variational fair autoencoder
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
GPP trains local variational encoders in federated settings to release representations that keep utility within 1% of an autoencoder baseline while driving adversary AUC on sensitive attributes to near-random levels on MNIST, CelebA, and HAPT data.
citing papers explorer
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Fair Dataset Distillation via Cross-Group Barycenter Alignment
Dataset distillation introduces fairness gaps from subgroup pattern mismatches rather than just imbalance; distilling to a group-agnostic barycenter of predictive information reduces these gaps.
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Distributed Deep Variational Approach for Privacy-preserving Data Release
GPP trains local variational encoders in federated settings to release representations that keep utility within 1% of an autoencoder baseline while driving adversary AUC on sensitive attributes to near-random levels on MNIST, CelebA, and HAPT data.