Introduces a fairness layer for deep learning models that guarantees output parity and an online primal-dual algorithm for aggregate fairness guarantees in streaming predictions with small batch sizes.
arXiv preprint arXiv:2012.04115 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Directional sharpness is introduced as a metric that correlates more strongly with generalization, identifies poor generalization more reliably, and supports efficient auditing and zero-knowledge certification.
citing papers explorer
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Differentiable Optimization Layers for Guaranteed Fairness in Deep Learning
Introduces a fairness layer for deep learning models that guarantees output parity and an online primal-dual algorithm for aggregate fairness guarantees in streaming predictions with small batch sizes.
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Certification of Machine Learning Models via Directional Sharpness
Directional sharpness is introduced as a metric that correlates more strongly with generalization, identifies poor generalization more reliably, and supports efficient auditing and zero-knowledge certification.