First optimal algorithm for fair top-k aggregation and 2-approximation for fair full rank aggregation under Spearman footrule (L1 distance).
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An algorithm learns a Mahalanobis metric from triplet queries via spectral initialization and gradient descent in the Bradley-Terry model, with convergence guarantees and transfer of individual fairness from estimated to true metric.
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Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking
First optimal algorithm for fair top-k aggregation and 2-approximation for fair full rank aggregation under Spearman footrule (L1 distance).
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Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models
An algorithm learns a Mahalanobis metric from triplet queries via spectral initialization and gradient descent in the Bradley-Terry model, with convergence guarantees and transfer of individual fairness from estimated to true metric.