VIF approximates leave-one-out retraining influence for non-decomposable losses (contrastive, ranking, Cox) using a finite-difference of the loss evaluated via auto-differentiation.
Specifically, we consider two methods used by Koh & Liang [2017], Conjugate Gradient (CG) and LiSSA [Agarwal et al., 2017]
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A Versatile Influence Function for Data Attribution with Non-Decomposable Loss
VIF approximates leave-one-out retraining influence for non-decomposable losses (contrastive, ranking, Cox) using a finite-difference of the loss evaluated via auto-differentiation.