ProF repairs DNNs for individual fairness by using interval bound propagation to bound outputs over input sets and solving a MILP to adjust the model with guarantees on those sets.
Equality of opportunity in supervised learning,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.
citing papers explorer
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Provable Fairness Repair for Deep Neural Networks
ProF repairs DNNs for individual fairness by using interval bound propagation to bound outputs over input sets and solving a MILP to adjust the model with guarantees on those sets.
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Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning
Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.