Kernel surrogate models with first-order gradient approximation achieve 25% higher correlation to leave-one-out ground truth for task attribution and 40% better downstream data selection than linear surrogates.
Extensions of lipshitz mapping into hilbert space
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Efficient Estimation of Kernel Surrogate Models for Task Attribution
Kernel surrogate models with first-order gradient approximation achieve 25% higher correlation to leave-one-out ground truth for task attribution and 40% better downstream data selection than linear surrogates.