RAIC unifies uniform recovery of structured signals from nonlinear observations via PGD, yielding error rates comparable to nonuniform guarantees up to log factors in sparse and 1-bit settings.
The Annals of Probability , pages=
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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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Robust Uniform Recovery of Structured Signals from Nonlinear Observations
RAIC unifies uniform recovery of structured signals from nonlinear observations via PGD, yielding error rates comparable to nonuniform guarantees up to log factors in sparse and 1-bit settings.
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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.