Risk-controlled post-processing yields a threshold-structured policy that follows the baseline except where an oracle fallback sharply reduces conditional violation risk, achieving O(log n/n) expected excess risk in i.i.d. settings and exact risk control under exchangeability.
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Clipped least-squares importance fitting enables weighted conformal prediction to achieve dataset-conditional coverage guarantees under unbounded covariate shifts by bounding undercoverage and estimating a corrective inflation factor from data.
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.
citing papers explorer
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Risk-Controlled Post-Processing of Decision Policies
Risk-controlled post-processing yields a threshold-structured policy that follows the baseline except where an oracle fallback sharply reduces conditional violation risk, achieving O(log n/n) expected excess risk in i.i.d. settings and exact risk control under exchangeability.
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Weight Clipping for Robust Conformal Inference under Unbounded Covariate Shifts
Clipped least-squares importance fitting enables weighted conformal prediction to achieve dataset-conditional coverage guarantees under unbounded covariate shifts by bounding undercoverage and estimating a corrective inflation factor from data.
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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.