We identify and address an overlooked challenge in the practical application of data attribution: hyperparameter tuning is tricky due to the costly evaluation metrics.

We derive a regularized representation of the influence function using the spectral theory of positive semidefinite kernels.

Propose a scalable gradient compression algorithm for data attribution with sub-linear complexity that achieves competitive attribution results.