Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
Retiring adult: New datasets for fair machine learning, 2022
4 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 4years
2026 4representative citing papers
Framework combining constrained density-ratio networks with anytime PAC-Bayes for covariate shift.
The work provides the first formal definitions of Rashomon sets for federated learning and introduces a multiplicity-aware training pipeline evaluated on standard benchmarks.
In a linear strategic-classification model where manipulation can genuinely improve outcomes, the optimal strategic classifier is a parallel shift of the Bayes boundary, and it is a provably better proxy for the improvement-aware objective than the Bayes classifier.
citing papers explorer
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Toward Calibrated, Fair, and accurate Deepfake Detection
Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
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Anytime PAC-Bayes for Constrained Density-Ratio Networks under Covariate Shift
Framework combining constrained density-ratio networks with anytime PAC-Bayes for covariate shift.
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Rashomon Sets and Model Multiplicity in Federated Learning
The work provides the first formal definitions of Rashomon sets for federated learning and introduces a multiplicity-aware training pipeline evaluated on standard benchmarks.
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Linear Strategic Classification with Endogenous Improvements
In a linear strategic-classification model where manipulation can genuinely improve outcomes, the optimal strategic classifier is a parallel shift of the Bayes boundary, and it is a provably better proxy for the improvement-aware objective than the Bayes classifier.